From c4ab13031ef0ea941fca988ce4320d42a26eade6 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:31:14 +0000 Subject: [PATCH 01/88] added required packages --- poetry.lock | 1067 +++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 4 +- 2 files changed, 1061 insertions(+), 10 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7ab73534..a5ae08f6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -195,6 +195,16 @@ files = [ {file = "ansicon-1.89.0.tar.gz", hash = "sha256:e4d039def5768a47e4afec8e89e83ec3ae5a26bf00ad851f914d1240b444d2b1"}, ] +[[package]] +name = "antlr4-python3-runtime" +version = "4.9.3" +description = "ANTLR 4.9.3 runtime for Python 3.7" +optional = false +python-versions = "*" +files = [ + {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, +] + [[package]] name = "anyio" version = "3.7.1" @@ -238,6 +248,25 @@ files = [ {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"}, ] +[[package]] +name = "asteroid-filterbanks" +version = "0.4.0" +description = "Asteroid's filterbanks" +optional = false +python-versions = ">=3.6" +files = [ + {file = "asteroid-filterbanks-0.4.0.tar.gz", hash = "sha256:415f89d1dcf2b13b35f03f7a9370968ac4e6fa6800633c522dac992b283409b9"}, + {file = "asteroid_filterbanks-0.4.0-py3-none-any.whl", hash = "sha256:4932ac8b6acc6e08fb87cbe8ece84215b5a74eee284fe83acf3540a72a02eaf5"}, +] + +[package.dependencies] +numpy = "*" +torch = ">=1.8.0" +typing-extensions = "*" + +[package.extras] +all = ["librosa", "scipy"] + [[package]] name = "asttokens" version = "2.4.1" @@ -286,6 +315,20 @@ tests = ["attrs[tests-no-zope]", "zope-interface"] tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] +[[package]] +name = "audioread" +version = "3.0.1" +description = "Multi-library, cross-platform audio decoding." +optional = false +python-versions = ">=3.6" +files = [ + {file = "audioread-3.0.1-py3-none-any.whl", hash = "sha256:4cdce70b8adc0da0a3c9e0d85fb10b3ace30fbdf8d1670fd443929b61d117c33"}, + {file = "audioread-3.0.1.tar.gz", hash = "sha256:ac5460a5498c48bdf2e8e767402583a4dcd13f4414d286f42ce4379e8b35066d"}, +] + +[package.extras] +test = ["tox"] + [[package]] name = "av" version = "10.0.0" @@ -770,6 +813,23 @@ files = [ [package.dependencies] colorama = {version = "*", markers = "platform_system == \"Windows\""} +[[package]] +name = "colorlog" +version = "6.8.0" +description = "Add colours to the output of Python's logging module." +optional = false +python-versions = ">=3.6" +files = [ + {file = "colorlog-6.8.0-py3-none-any.whl", hash = "sha256:4ed23b05a1154294ac99f511fabe8c1d6d4364ec1f7fc989c7fb515ccc29d375"}, + {file = "colorlog-6.8.0.tar.gz", hash = "sha256:fbb6fdf9d5685f2517f388fb29bb27d54e8654dd31f58bc2a3b217e967a95ca6"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "sys_platform == \"win32\""} + +[package.extras] +development = ["black", "flake8", "mypy", "pytest", "types-colorama"] + [[package]] name = "comm" version = "0.2.1" @@ -988,6 +1048,27 @@ files = [ {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"}, ] +[[package]] +name = "docopt" +version = "0.6.2" +description = "Pythonic argument parser, that will make you smile" +optional = false +python-versions = "*" +files = [ + {file = "docopt-0.6.2.tar.gz", hash = "sha256:49b3a825280bd66b3aa83585ef59c4a8c82f2c8a522dbe754a8bc8d08c85c491"}, +] + +[[package]] +name = "einops" +version = "0.7.0" +description = "A new flavour of deep learning operations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "einops-0.7.0-py3-none-any.whl", hash = "sha256:0f3096f26b914f465f6ff3c66f5478f9a5e380bb367ffc6493a68143fbbf1fd1"}, + {file = "einops-0.7.0.tar.gz", hash = "sha256:b2b04ad6081a3b227080c9bf5e3ace7160357ff03043cd66cc5b2319eb7031d1"}, +] + [[package]] name = "exceptiongroup" version = "1.2.0" @@ -1038,26 +1119,32 @@ all = ["email-validator (>=2.0.0)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)" [[package]] name = "faster-whisper" -version = "0.9.0" +version = "0.10.0" description = "Faster Whisper transcription with CTranslate2" optional = false python-versions = ">=3.8" -files = [ - 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"https://github.com/mobiusml/faster-whisper.git", tag = "v0.10.0_mobiusml" } onnxruntime = "1.16.1" opencv-python = "^4.8.1.78" +pyannote-audio = "^3.1.1" pydantic = "<2" pytest-mock = "^3.12.0" python-multipart = "^0.0.6" @@ -29,6 +30,7 @@ scipy = "^1.11.3" sqlalchemy = {extras = ["mypy"], version = "^2.0.23"} transformers = "^4.34.1" torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl" } +torchaudio = {url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl"} torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl" } vllm = "^0.2.1.post1" yt-dlp = "^2023.10.13" From 280a7c8b96f85f3b4ec165c25a528f8ee8bc2bdf Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:31:42 +0000 Subject: [PATCH 02/88] added endpoint for transcribe_batch --- aana/configs/endpoints.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/aana/configs/endpoints.py b/aana/configs/endpoints.py index 9763052c..8ff49392 100644 --- a/aana/configs/endpoints.py +++ b/aana/configs/endpoints.py @@ -124,6 +124,36 @@ ), ], ), + Endpoint( + name="whisper_transcribe", + path="/video/transcribe_batch", + summary="Transcribe a video using Whisper Medium in batch mode", + outputs=[ + EndpointOutput( + name="vad_segments", + output="video_transcriptions_vad_segments", + ), + EndpointOutput( + name="transcription", + output="video_transcriptions_batched_whisper_medium", + streaming=True, + ), + EndpointOutput( + name="segments", + output="video_transcriptions_segments_batched_whisper_medium", + streaming=True, + ), + EndpointOutput( + name="info", + output="video_transcriptions_info_batched_whisper_medium", + streaming=True, + ), + # EndpointOutput( + # name="transcription_id_batched", output="transcription_id_batched" + # ), + ], + streaming=True, + ), ], "chat_with_video_test": [ Endpoint( From a838ca44fb16664e61241653c109267598dc4f1e Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:32:14 +0000 Subject: [PATCH 03/88] added vad deployment details --- aana/configs/deployments.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/aana/configs/deployments.py b/aana/configs/deployments.py index b9337b98..d2a266bf 100644 --- a/aana/configs/deployments.py +++ b/aana/configs/deployments.py @@ -6,6 +6,8 @@ WhisperDeployment, WhisperModelSize, ) +from aana.deployments.vad_deployment import VadDeployment, VadConfig + from aana.models.core.dtype import Dtype from aana.models.pydantic.sampling_params import SamplingParams @@ -45,4 +47,10 @@ compute_type=WhisperComputeType.FLOAT16, ).dict(), ), + "vad_deployment": VadDeployment.options( + num_replicas=1, + max_concurrent_queries=1000, + ray_actor_options={"num_gpus": 0.25}, + user_config=VadConfig(model_fp=None).dict(), + ), } From 83396008531ce530afd5e99b5c63284a223b04d4 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:32:59 +0000 Subject: [PATCH 04/88] added nodes for batched inference and storage --- aana/configs/pipeline.py | 125 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) diff --git a/aana/configs/pipeline.py b/aana/configs/pipeline.py index 426de897..64bbf823 100644 --- a/aana/configs/pipeline.py +++ b/aana/configs/pipeline.py @@ -11,6 +11,7 @@ AsrTranscriptionInfoList, AsrTranscriptionList, ) +from aana.models.pydantic.vad_output import VadSegments from aana.models.pydantic.captions import CaptionsList, VideoCaptionsList from aana.models.pydantic.chat_message import ChatDialog from aana.models.pydantic.image_input import ImageInputList @@ -20,6 +21,7 @@ from aana.models.pydantic.video_metadata import VideoMetadata from aana.models.pydantic.video_params import VideoParams from aana.models.pydantic.whisper_params import WhisperParams +from aana.models.pydantic.vad_params import VadParams # container data model # we don't enforce this data model for now but it's a good reference for writing paths and flatten_by @@ -516,6 +518,91 @@ }, ], }, + { + "name": "vad_params", + "type": "input", + "inputs": [], + "outputs": [ + { + "name": "vad_params", + "key": "vad_params", + "path": "video_batch.vad_params", + "data_model": VadParams, + } + ], + }, + { + "name": "vad_batched_inference_video", + "type": "ray_deployment", + "deployment_name": "vad_deployment", + "method": "asr_preprocess_vad", + "inputs": [ + { + "name": "video_object", + "key": "media", + "path": "video.video", + }, + { + "name": "vad_params", + "key": "params", + "path": "video_batch.vad_params", + }, + ], + "outputs": [ + { + "name": "video_transcriptions_vad_segments", + "key": "vad_segments", + "path": "video.vad_segments", + "data_model": VadSegments, + }, + ], + }, + { + "name": "whisper_medium_batched_inference_video", + "type": "ray_deployment", + "deployment_name": "whisper_deployment_medium", + "data_type": "generator", + "generator_path": "video", + "method": "batched_inference", + "inputs": [ + { + "name": "video_object", + "key": "media", + "path": "video.video", + }, + { + "name": "video_transcriptions_vad_segments", + "key": "vad_segments", + "path": "video.vad_segments", + }, + { + "name": "whisper_params", + "key": "params", + "path": "video_batch.whisper_params", + "data_model": WhisperParams, + }, + ], + "outputs": [ + { + "name": "video_transcriptions_segments_batched_whisper_medium", + "key": "segments", + "path": "video.segments_batched", + "data_model": AsrSegments, + }, + { + "name": "video_transcriptions_info_batched_whisper_medium", + "key": "transcription_info", + "path": "video.transcription_info_batched", + "data_model": AsrTranscriptionInfo, + }, + { + "name": "video_transcriptions_batched_whisper_medium", + "key": "transcription", + "path": "video.transcription_batched", + "data_model": AsrTranscription, + }, + ], + }, { "name": "media_id", "type": "input", @@ -825,6 +912,44 @@ } ], }, + { + "name": "save_video_transcription_batched", + "type": "function", + "function": "aana.utils.db.save_video_transcription", + "kwargs": { + "model_name": "whisper_medium", + }, + "dict_output": True, + "inputs": [ + { + "name": "video_media_id", + "key": "media_id", + "path": "video.media_id", + }, + { + "name": "video_transcriptions_info_batched_whisper_medium", + "key": "transcription_info", + "path": "video.transcription_info_batched", + }, + { + "name": "video_transcriptions_segments_batched_whisper_medium", + "key": "segments", + "path": "video.segments_batched", + }, + { + "name": "video_transcriptions_whisper_medium", + "key": "transcription", + "path": "video.transcription_batched", + }, + ], + "outputs": [ + { + "name": "transcription_id_batched", + "key": "transcription_id", + "path": "video.transcription_id_batched", + } + ], + }, { "name": "save_transcripts_batch_medium", "type": "function", From d68d84a1e1b8ca1edb82cbc8bfcd1b6ccf8ae115 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:33:28 +0000 Subject: [PATCH 05/88] vad deployment for segmenting the audio --- aana/deployments/vad_deployment.py | 478 +++++++++++++++++++++++++++++ 1 file changed, 478 insertions(+) create mode 100644 aana/deployments/vad_deployment.py diff --git a/aana/deployments/vad_deployment.py b/aana/deployments/vad_deployment.py new file mode 100644 index 00000000..20b4c111 --- /dev/null +++ b/aana/deployments/vad_deployment.py @@ -0,0 +1,478 @@ +from pathlib import Path +from contextlib import ExitStack +from collections.abc import Callable +import urllib.request, hashlib +from tqdm import tqdm +from typing import Callable, TypedDict, Optional, Text, Union, Any + +import torch +import subprocess +import numpy as np + +# faster whisper here? or load audio? +from pyannote.audio import Model +from pyannote.audio.core.io import AudioFile +from pyannote.audio.pipelines import VoiceActivityDetection +from pyannote.audio.pipelines.utils import PipelineModel +from pyannote.core import Annotation, Segment, SlidingWindowFeature + +from aana.models.core.video import Video +from aana.deployments.base_deployment import BaseDeployment +from aana.exceptions.general import InferenceException +from aana.models.pydantic.vad_params import VadParams +from aana.models.pydantic.vad_output import VadSegment +from aana.models.pydantic.time_interval import TimeInterval + +from ray import serve +from faster_whisper.audio import decode_audio +from pydantic import BaseModel, Field + + +class SegmentX: + def __init__(self, start, end, speaker=None): + self.start = start + self.end = end + self.speaker = speaker + + +class VadOutput(TypedDict): + """The output of the VAD model. + + Attributes: + segments (list[VadSegment]): The VAD segments. + """ + + vad_segments: list[VadSegment] + + +class VadConfig(BaseModel): + """The configuration for the whisper deployment from faster-whisper.""" + + model_fp: Path | None = Field(default=None, description="The VAD model path.") + + +class VoiceActivitySegmentation(VoiceActivityDetection): + """Pipeline for performing Voice Activity Segmentation based on the detection from the vad model. + + Args: + dict parameters of VoiceActivityDetection class from pyannote: + segmentation: loaded model. + device: torch.device to perform the segmentation. + + Returns: + segmentations: segmented speech regions + """ + + def __init__( + self, + segmentation: PipelineModel = "pyannote/segmentation", + fscore: bool = False, + use_auth_token: Union[Text, None] = None, + **inference_kwargs, + ): + super().__init__( + segmentation=segmentation, + fscore=fscore, + use_auth_token=use_auth_token, + **inference_kwargs, + ) + + def apply(self, file: AudioFile, hook: Optional[Callable] = None) -> Annotation: + """Apply voice activity detection. + + Args: + file (AudioFile): Processed file. + hook (callable, optional): Hook called after each major step of the pipeline with the following signature: hook("step_name", step_artefact, file=file) + + Returns: + speech (Annotation): Speech regions. + """ + # setup hook (e.g. for debugging purposes) + hook = self.setup_hook(file, hook=hook) + + # apply segmentation model (only if needed) + # output shape is (num_chunks, num_frames, 1) + if self.training: + if self.CACHED_SEGMENTATION in file: + segmentations = file[self.CACHED_SEGMENTATION] + else: + segmentations = self._segmentation(file) + file[self.CACHED_SEGMENTATION] = segmentations + else: + segmentations: SlidingWindowFeature = self._segmentation(file) + + return segmentations + + +class Binarize: + """Binarize detection scores using hysteresis thresholding, with min-cut operation to ensure no segments are longer than max_duration. + + Args: + onset (float, optional): + Onset threshold. Defaults to 0.5. + offset (float, optional): + Offset threshold. Defaults to `onset`. + min_duration_on (float, optional): + Remove active regions shorter than that many seconds. Defaults to 0s. + min_duration_off (float, optional): + Fill inactive regions shorter than that many seconds. Defaults to 0s. + pad_onset (float, optional): + Extend active regions by moving their start time by that many seconds. + Defaults to 0s. + pad_offset (float, optional): + Extend active regions by moving their end time by that many seconds. + Defaults to 0s. + max_duration (float): + The maximum length of an active segment, divides segment at timestamp with lowest score. + + Reference: + Gregory Gelly and Jean-Luc Gauvain. "Minimum Word Error Training of + RNN-based Voice Activity Detection", InterSpeech 2015. + + Modified by Max Bain to include WhisperX's min-cut operation + https://arxiv.org/abs/2303.00747 + + """ + + def __init__( + self, + onset: float = 0.5, + offset: Optional[float] = None, + min_duration_on: float = 0.0, + min_duration_off: float = 0.0, + pad_onset: float = 0.0, + pad_offset: float = 0.0, + max_duration: float = float("inf"), + ): + super().__init__() + + self.onset = onset + self.offset = offset or onset + + self.pad_onset = pad_onset + self.pad_offset = pad_offset + + self.min_duration_on = min_duration_on + self.min_duration_off = min_duration_off + + self.max_duration = max_duration + + def __call__(self, scores: SlidingWindowFeature) -> Annotation: + """Binarize detection scores. + + Args: + scores : SlidingWindowFeature + Detection scores. + + Returns: + active : Annotation + Binarized scores. + """ + num_frames, num_classes = scores.data.shape + frames = scores.sliding_window + timestamps = [frames[i].middle for i in range(num_frames)] + + # annotation meant to store 'active' regions + active = Annotation() + for k, k_scores in enumerate(scores.data.T): + label = k if scores.labels is None else scores.labels[k] + + # initial state + start = timestamps[0] + is_active = k_scores[0] > self.onset + curr_scores = [k_scores[0]] + curr_timestamps = [start] + t = start + for t, y in zip(timestamps[1:], k_scores[1:]): + # currently active + if is_active: + curr_duration = t - start + if curr_duration > self.max_duration: + search_after = len(curr_scores) // 2 + # divide segment + min_score_div_idx = search_after + np.argmin( + curr_scores[search_after:] + ) + min_score_t = curr_timestamps[min_score_div_idx] + region = Segment( + start - self.pad_onset, min_score_t + self.pad_offset + ) + active[region, k] = label + start = curr_timestamps[min_score_div_idx] + curr_scores = curr_scores[min_score_div_idx + 1 :] + curr_timestamps = curr_timestamps[min_score_div_idx + 1 :] + # switching from active to inactive + elif y < self.offset: + region = Segment(start - self.pad_onset, t + self.pad_offset) + active[region, k] = label + start = t + is_active = False + curr_scores = [] + curr_timestamps = [] + curr_scores.append(y) + curr_timestamps.append(t) + # currently inactive + else: + # switching from inactive to active + if y > self.onset: + start = t + is_active = True + + # if active at the end, add final region + if is_active: + region = Segment(start - self.pad_onset, t + self.pad_offset) + active[region, k] = label + + # because of padding, some active regions might be overlapping: merge them. + # also: fill same speaker gaps shorter than min_duration_off + if self.pad_offset > 0.0 or self.pad_onset > 0.0 or self.min_duration_off > 0.0: + if self.max_duration < float("inf"): + raise NotImplementedError("This would break current max_duration param") + active = active.support(collar=self.min_duration_off) + + # remove tracks shorter than min_duration_on + if self.min_duration_on > 0: + for segment, track in list(active.itertracks()): + if segment.duration < self.min_duration_on: + del active[segment, track] + + return active + + +@serve.deployment +class VadDeployment(BaseDeployment): + """Deployment to serve VAD models.""" + + async def apply_config(self, config: dict[str, Any]): + """Apply the configuration. + + The method is called when the deployment is created or updated. + + It loads the model from from the default open url. + + The configuration should conform to the VadConfig schema. + + """ + + def download_vad_model(model_fp, vad_segmentation_url): + if model_fp is None: + model_dir = torch.hub._get_torch_home() + if not Path(model_dir).exists(): + Path(model_dir).mkdir(parents=True) + model_fp = Path(model_dir) / "whisperx-vad-segmentation.bin" + + if Path(model_fp).exists() and not Path(model_fp).is_file(): + raise RuntimeError(f"{model_fp}") # exists and is not a regular file + + if not Path(model_fp).is_file(): + with ExitStack() as stack: + source = stack.enter_context( + urllib.request.urlopen(vad_segmentation_url) + ) + output = stack.enter_context(Path.open(model_fp, "wb")) + + loop = tqdm( + total=int(source.info().get("Content-Length")), + ncols=80, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) + + with loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = Path.open(model_fp, "rb").read() + + if ( + hashlib.sha256(model_bytes).hexdigest() + != vad_segmentation_url.split("/")[-2] + ): + checksum_error = "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." + raise RuntimeError(f"{checksum_error}") + + return model_fp + + config_obj = VadParams(**config) + self.device = "cuda" if torch.cuda.is_available() else "cpu" + # print(config_obj.model_fp) + # print(config_obj.vad_segmentation_url) + filepath = download_vad_model( + config_obj.model_fp, config_obj.vad_segmentation_url + ) + + self.vad_model = Model.from_pretrained(filepath, use_auth_token=None) + hyperparameters = { + "onset": config_obj.onset, + "offset": config_obj.offset, + "min_duration_on": config_obj.min_duration_on, + "min_duration_off": config_obj.min_duration_off, + } + + # vad_pipeline + self.vad_pipeline = VoiceActivitySegmentation( + segmentation=self.vad_model, device=torch.device(self.device) + ) + self.vad_pipeline.instantiate(hyperparameters) + + # OPTIONAL: audio loader function if asr model is not used in deployment.:can not use subprocess in an async function: use whisper ftb + async def load_audio(self, file: str, sr: int = 16000): + """Open an audio file and read as mono waveform, resampling as necessary. + + Args: + file (str): The audio file to open. + sr (int): The sample rate to resample the audio if necessary. + + Returns: + A NumPy array containing the audio waveform, in float32 dtype. + + Raises: + RuntimeError: if ffmpeg fails to convert and load the audio. + """ + try: + # Launches a subprocess to decode audio while down-mixing and resampling as necessary. + # Requires the ffmpeg CLI to be installed. + cmd = [ + "ffmpeg", + "-nostdin", + "-threads", + "0", + "-i", + file, + "-f", + "s16le", + "-ac", + "1", + "-acodec", + "pcm_s16le", + "-ar", + str(sr), + "-", + ] + out = subprocess.run(cmd, capture_output=True, check=True).stdout + except subprocess.CalledProcessError as e: + raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e + + return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 + + async def merge_chunks( + self, segments: list[dict], params: dict[str, Any] + ) -> list[VadSegment]: + """Merge operation to chunk_size segments. + + Args: + segments (List(dict)): The list of segments to merge/split. + params (dict[str, Any]): The parameters for the vad model as a dictionary + + Returns: + merged_segments list(VadSegment): The list of segments. + + Raises: + InferenceException: If the inference fails. + """ + chunk_size = params["chunk_size"] + onset = params["merge_onset"] + offset = params["merge_offset"] + + curr_end = 0 + merged_segments: list[VadSegment] = [] + seg_idxs: list[tuple[float, float]] = [] + speaker_idxs = [str] + + if not chunk_size > 0: + chunk_error = "Expected positive value." + raise ValueError(f"{chunk_error}") + + binarize = Binarize(max_duration=chunk_size, onset=onset, offset=offset) + segments = binarize(segments) + segments_list = [] + for speech_turn in segments.get_timeline(): + segments_list.append( # noqa: PERF401 + SegmentX( + max(0.0, speech_turn.start - 0.1), speech_turn.end + 0.1, "UNKNOWN" + ) + ) # To account for edge errors additional 100ms padding + + if len(segments_list) == 0: + print("No active speech found in audio") + return [] + + # Make sure the starting point is the start of the segment. + curr_start = segments_list[0].start + + for seg in segments_list: + if seg.end - curr_start > chunk_size and curr_end - curr_start > 0: + merged_segments.append( + VadSegment( + time_interval=TimeInterval(start=curr_start, end=curr_end), + segments=seg_idxs, + ) + ) + curr_start = seg.start + seg_idxs = [] + speaker_idxs = [] + curr_end = seg.end + seg_idxs.append((seg.start, seg.end)) + speaker_idxs.append(seg.speaker) + + merged_segments.append( + VadSegment( + time_interval=TimeInterval(start=curr_start, end=curr_end), + segments=seg_idxs, + ) + ) + return merged_segments + + async def vad_inference(self, media: Video) -> list[dict]: + """Perform voice activity detection on the media with the vad model. + + Args: + media (Video): The media to perform vad. + + Returns: + vad_segments (VadSegment): The list of vad segments. + + Raises: + InferenceException: If the vad inference fails. + """ + media_path: str = str(media.path) + audio_array = decode_audio(media_path) # self.load_audio() has subprocess issue + # TODO: raise valueError if not able to load + try: + vad_segments = self.vad_pipeline( + { + "waveform": torch.from_numpy(audio_array).unsqueeze(0), + "sample_rate": 16000, + } + ) + except Exception as e: + raise InferenceException(self.vad_model) from e + + return vad_segments + + async def asr_preprocess_vad( + self, media: Video, params: VadParams | None = None + ) -> VadOutput: + """Perform vad inference and further processing for batched asr inference. + + Args: + media (Video): The media to perform vad. + params (VadParams): The parameters for the vad model. + + Returns: + vad_segments (VadOutput): Output vad segments to the asr model. + """ + if not params: + params = VadParams() + + intermediate_vad_segments = await self.vad_inference(media) + asr_vad_segments = await self.merge_chunks( + intermediate_vad_segments, params.__dict__ + ) + return VadOutput(vad_segments=asr_vad_segments) From 34681a70763770c8dcdddb37721ae44b73190af0 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:33:58 +0000 Subject: [PATCH 06/88] modified whisper deployment for batched_inference --- aana/deployments/whisper_deployment.py | 103 ++++++++++++++++++++++++- 1 file changed, 100 insertions(+), 3 deletions(-) diff --git a/aana/deployments/whisper_deployment.py b/aana/deployments/whisper_deployment.py index d60e7a07..4aa9bdcd 100644 --- a/aana/deployments/whisper_deployment.py +++ b/aana/deployments/whisper_deployment.py @@ -1,9 +1,16 @@ from collections.abc import AsyncGenerator from enum import Enum +import numpy as np +from types import SimpleNamespace from typing import Any, TypedDict, cast import torch -from faster_whisper import WhisperModel +from faster_whisper import WhisperModel, BatchedInferencePipeline +from faster_whisper.tokenizer import Tokenizer +from faster_whisper.transcribe import TranscriptionOptions +from faster_whisper.audio import decode_audio + + from pydantic import BaseModel, Field from ray import serve @@ -15,7 +22,12 @@ AsrTranscription, AsrTranscriptionInfo, ) -from aana.models.pydantic.whisper_params import WhisperParams +from aana.models.pydantic.whisper_params import ( + WhisperParams, + default_batched_asr_options, +) + +from aana.models.pydantic.vad_output import VadSegment class WhisperComputeType(str, Enum): @@ -90,7 +102,7 @@ class WhisperOutput(TypedDict): """The output of the whisper model. Attributes: - segments (List[AsrSegment]): The ASR segments. + segments (list[AsrSegment]): The ASR segments. transcription_info (AsrTranscriptionInfo): The ASR transcription info. transcription (AsrTranscription): The ASR transcription. """ @@ -240,3 +252,88 @@ async def transcribe_batch( return WhisperBatchOutput( segments=segments, transcription_info=infos, transcription=transcriptions ) + + async def batched_inference( + self, + media: Video, + vad_segments: list[VadSegment], + batch_size: int = 16, + params: WhisperParams | None = None, + ) -> AsyncGenerator[WhisperOutput, None]: + """Transcribe the batch of audio segments with the Whisper model. + + Args: + media (Video): The media to transcribe. / audio_data (np.ndarray): The audio numpy array to transcribe. + vad_segments (list[VadSegment]): List of segments to guide batching the audio data. + batch_size (int): Maximum batch size for the batched inference. + params (WhisperParams): The parameters for the whisper model. + + + Yields: + WhisperOutput: The transcription output as a dictionary: + segments (List[AsrSegment]): The ASR segments. + transcription_info (AsrTranscriptionInfo): The ASR transcription info. + transcription (AsrTranscription): The ASR transcription. + + Raises: + InferenceException: If the inference fails. + """ + if not params: + params = WhisperParams() + + # additional param: batch size for batched asr + if params.language is not None: + tokenizer = Tokenizer( + self.model.hf_tokenizer, + self.model.model.is_multilingual, + task=params.task, + language=params.language, + ) + else: + self.model.logger.warning( + "No language specified, language will be first detected for each audio file." + ) + tokenizer = None + + default_asr_options = TranscriptionOptions(**default_batched_asr_options) + + self.batched_model = BatchedInferencePipeline( + model=self.model, + options=default_asr_options, + tokenizer=tokenizer, + language=params.language, + ) + + media_path: str = str(media.path) + audio_data = decode_audio(media_path) + # TODO: raise value error if not able to load the audio + + # dict_format = kp['vad_segments'][0].to_dict() + vad_input = [seg.to_dict() for seg in vad_segments] + # print(vad_input[0]) + try: + result = self.batched_model.transcribe_stream( + audio_data, + vad_segments=vad_input, + batch_size=batch_size, + ) + except Exception as e: + raise InferenceException(self.model_name) from e + + count = 0 + for segment, info in result: + if count == 0: + # converting info dictionary to an object: + transcription_object = SimpleNamespace(**info) + asr_transcription_info = AsrTranscriptionInfo.from_whisper( + transcription_object + ) + count += 1 + asr_segments = [AsrSegment.from_whisper(segment)] + asr_transcription = AsrTranscription(text=segment.text) + + yield WhisperOutput( + segments=asr_segments, + transcription_info=asr_transcription_info, + transcription=asr_transcription, + ) From ab89fcfbac76ca080e6cfc6ed61adc48c3a62e76 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:34:27 +0000 Subject: [PATCH 07/88] modify output datamodel to accomodate batched inference --- aana/models/pydantic/asr_output.py | 29 +++++++++++++++++++---------- 1 file changed, 19 insertions(+), 10 deletions(-) diff --git a/aana/models/pydantic/asr_output.py b/aana/models/pydantic/asr_output.py index 634821ef..356b1314 100644 --- a/aana/models/pydantic/asr_output.py +++ b/aana/models/pydantic/asr_output.py @@ -1,4 +1,5 @@ from types import MappingProxyType # for immutable dictionary +from typing import Union import numpy as np from faster_whisper.transcribe import ( @@ -54,18 +55,20 @@ class AsrSegment(BaseModel): Attributes: text (str): The text of the segment (transcript/translation) time_interval (TimeInterval): Time interval of the segment - confidence (float): Confidence of the segment - no_speech_confidence (float): Chance of being a silence segment - words (List[AsrWord]): List of words in the segment + confidence (float): Confidence of the segment (Optional) + no_speech_confidence (float): Chance of being a silence segment (Optional) + words (List[AsrWord]): List of words in the segment (Optional) """ text: str = Field(description="The text of the segment (transcript/translation)") time_interval: TimeInterval = Field(description="Time interval of the segment") - confidence: float = Field(ge=0.0, le=1.0, description="Confidence of the segment") - no_speech_confidence: float = Field( + confidence: float | None = Field( + ge=0.0, le=1.0, description="Confidence of the segment" + ) + no_speech_confidence: float | None = Field( ge=0.0, le=1.0, description="Chance of being a silence segment" ) - words: list[AsrWord] = Field( + words: list[AsrWord] | None = Field( description="List of words in the segment", default_factory=list ) @@ -75,17 +78,23 @@ def from_whisper(cls, whisper_segment: WhisperSegment) -> "AsrSegment": time_interval = TimeInterval( start=whisper_segment.start, end=whisper_segment.end ) - confidence = np.exp(whisper_segment.avg_logprob) - if whisper_segment.words: + avg_logprob = getattr(whisper_segment, "avg_logprob", None) + confidence = np.exp(avg_logprob) if avg_logprob else None + + whisper_words = getattr(whisper_segment, "words", None) + if whisper_words: words = [AsrWord.from_whisper(word) for word in whisper_segment.words] else: - words = [] + words = None + + no_speech_prob = getattr(whisper_segment, "no_speech_prob", None) + no_speech_confidence = no_speech_prob if no_speech_prob else None return cls( text=whisper_segment.text, time_interval=time_interval, confidence=confidence, - no_speech_confidence=whisper_segment.no_speech_prob, + no_speech_confidence=no_speech_confidence, words=words, ) From 3c890951797240c2358aba58760bda2eccf0a9d9 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:35:25 +0000 Subject: [PATCH 08/88] output datamodel for vad_deployment --- aana/models/pydantic/vad_output.py | 46 ++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 aana/models/pydantic/vad_output.py diff --git a/aana/models/pydantic/vad_output.py b/aana/models/pydantic/vad_output.py new file mode 100644 index 00000000..9bd8f16b --- /dev/null +++ b/aana/models/pydantic/vad_output.py @@ -0,0 +1,46 @@ +from types import MappingProxyType # for immutable dictionary +from aana.models.pydantic.base import BaseListModel +from pydantic import BaseModel, Field +from aana.models.pydantic.time_interval import TimeInterval + + +class VadSegment(BaseModel): + """Pydantic schema for Segment from Voice Activity Detection model. + + Attributes: + time_interval (TimeInterval): The start and end time of the segment + segments (list[tuple[float, float]]): vad segments within a merged vad segment + """ + + time_interval: TimeInterval = Field(description="Time interval of the segment") + segments: list[tuple[float, float]] = Field(description="VAD Segment for ASR") + + def to_dict(self): + """Generate dict from VADSegment.""" + return { + "start": self.time_interval.start, + "end": self.time_interval.end, + "segments": self.segments, + } + + class Config: + schema_extra = MappingProxyType( + { + "description": "VAD Segment for ASR", + } + ) + + +class VadSegments(BaseListModel): + """Pydantic schema for the list of ASR segments.""" + + __root__: list[VadSegment] = Field( + description="List of VAD segments", default_factory=list + ) + + class Config: + schema_extra = MappingProxyType( + { + "description": "List of VAD segments", + } + ) From b3e6fc5946434d1ee1c03594f4ecfdc334e9a32c Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:36:06 +0000 Subject: [PATCH 09/88] parameters of the vad model and inference --- aana/models/pydantic/vad_params.py | 66 ++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 aana/models/pydantic/vad_params.py diff --git a/aana/models/pydantic/vad_params.py b/aana/models/pydantic/vad_params.py new file mode 100644 index 00000000..0284701c --- /dev/null +++ b/aana/models/pydantic/vad_params.py @@ -0,0 +1,66 @@ +from pydantic import BaseModel, Field +from types import MappingProxyType + +from pathlib import Path + + +class VadParams(BaseModel): + """A model for the Voice Activity Detection model parameters. + + Attributes: + model_fp (str): Path to the VAD model file. + vad_segmentation_url (str): Model source location to download the model if model_fp is None. + onset (float): Threshold to decide a positive voice activity. + offset (float): Threshold to consider as a silence region. + min_duration_on (float): Minimum voiced duration. + min_duration_off (float): "Minimum duration to consider as silence. + """ + + model_fp: Path | None = Field( + default=None, # "/nas/jilt/models/vad/whisper-x_vad/pytorch_model.bin", # None + description="Model file path.", + ) + + vad_segmentation_url: str = Field( + default="https://whisperx.s3.eu-west-2.amazonaws.com/model_weights/segmentation/0b5b3216d60a2d32fc086b47ea8c67589aaeb26b7e07fcbe620d6d0b83e209ea/pytorch_model.bin", + description="Model source to download from.", + ) + + onset: float = Field( + default=0.500, + ge=0.0, + description="Threshold to decide a positive voice activity.", + ) + + offset: float = Field( + default=0.363, + ge=0.0, + description="Threshold to consider as a silence region.", + ) + + min_duration_on: float = Field( + default=0.1, ge=0.0, description="Minimum voiced duration." + ) + + min_duration_off: float = Field( + default=0.1, ge=0.0, description="Minimum duration to consider as silence." + ) + + # for the merge_chunks function: + chunk_size: float = Field( + default=30, ge=10.0, description="The maximum lenth of each vad output chunk." + ) + + merge_onset: float = Field( + default=0.5, ge=0.0, description="Onset to be used for the merging operation." + ) + + merge_offset: float | None = Field( + default=None, + description="Optional offset to be used for the merging operation.", + ) + + class Config: + schema_extra = MappingProxyType( + {"description": "Parameters for the voice activity detection model."} + ) From b642373d19070dfa01bc88be6ead48a0eda75ba1 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:36:56 +0000 Subject: [PATCH 10/88] added default parameters for batched asr --- aana/models/pydantic/whisper_params.py | 28 ++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/aana/models/pydantic/whisper_params.py b/aana/models/pydantic/whisper_params.py index 28f92111..a7d7920f 100644 --- a/aana/models/pydantic/whisper_params.py +++ b/aana/models/pydantic/whisper_params.py @@ -72,3 +72,31 @@ class Config: schema_extra = MappingProxyType( {"description": "Parameters for the Whisper audio-to-text model."} ) + + +default_batched_asr_options = { + "beam_size": 5, + "best_of": 5, + "patience": 1, + "length_penalty": 1, + "repetition_penalty": 1, + "no_repeat_ngram_size": 0, + "temperatures": [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + "compression_ratio_threshold": 2.4, + "log_prob_threshold": -1.0, + "no_speech_threshold": 0.6, + "condition_on_previous_text": False, + "prompt_reset_on_temperature": 0.5, + "initial_prompt": None, + "prefix": None, + "suppress_blank": True, + "suppress_tokens": [-1], + "without_timestamps": True, # False for timings + "max_initial_timestamp": 0.0, + "word_timestamps": False, + "prepend_punctuations": "\"'“¿([{-", + "append_punctuations": "\"'.。,，!！?？:：”)]}、", + "log_prob_low_threshold": -2.0, + "multilingual": False, + "output_language": "en", +} From 1d6087d8f9386a7640ed1fb21afa1417a8c2b196 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 26 Jan 2024 16:37:17 +0000 Subject: [PATCH 11/88] added example in the demo --- notebooks/demo.ipynb | 167 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 159 insertions(+), 8 deletions(-) diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb index 70d7909b..b18d59b9 100644 --- a/notebooks/demo.ipynb +++ b/notebooks/demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -121,7 +121,16 @@ } ], "source": [ - "# whisper transcrib stream example\n", + "# whisper transcribe stream example\n", + "data = {\n", + " \"video\": {\"path\": \"/workspaces/aana_sdk/aana/tests/files/videos/physicsworks.webm\"},\n", + "}\n", + "\n", + "data = {\n", + " \"video\": {\n", + " \"path\": \"/nas/jilt/datasets/Documentaries/Crawler/all_audios/Disco Changed Everything. So Why Did It Die-LO_EF1BY6Tg.wav\"\n", + " },\n", + "}\n", "data = {\n", " \"video\": {\"path\": \"/workspaces/aana_sdk/aana/tests/files/videos/physicsworks.webm\"},\n", "}\n", @@ -132,7 +141,6 @@ "\n", "for chunk in response.iter_content(chunk_size=None):\n", " json_data = json.loads(chunk)\n", - " # currently shows validation error on uncommented version\n", "\n", " if \"error\" in json_data:\n", " print(json_data[\"error\"])\n", @@ -146,15 +154,158 @@ " else:\n", " print(\"\\n\")\n", " print(\"===================\")\n", - " print(\"End of Transcription\")" + " print(\"End of Transcription\")\n", + " # 27.2 sec disco" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " This is one of my favorite opening scenes to a movie. It's from 1977's Saturday Night Fever. Tony Manero's walk, the Bee Gees in the background, the paint can, double stacked pizza. This.\n", + "\n", + "\n", + " I love this scene because there's this weird sense of glamour to everything. And that's actually pretty reflective of the moment in history this film tries to capture. A moment in the 70s that left a lasting impact on American sexuality, especially the American man, and prompted a backlash.\n", + "\n", + "\n", + " That beat is known as Four on the Floor, created by drummer Earl Young, and it changed the way we danced. And the genesis of the beat was, according to Young in a BBC interview, something kind of mundane. It's just like a walking move like this. It's a walking feel. So when people hear that, you automatically want to move something. And I say, well, gee, this is pretty cool. So I said, I'm going to put this to a groove.\n", + "\n", + "\n", + " Now here's Young playing with the tramps on a classic disco hit that you probably will recognize. That's the sound that most people would categorize as being disco. But the type of music people were dancing to in actual discos in the 70s didn't always fit that description.\n", + "\n", + "\n", + " Disco was a very eclectic sort of music. It started out being a little bit of everything, and I think at its best it still remains very, very unpredictable. And it was those at the margins of society who really defined the direction that disco would take.\n", + "\n", + "\n", + " The venues where disco flourished and first took root and the communities that fed into that and that served as its dancers, as its music producers, as its DJs and so on came directly out of especially black and brown, Latinx communities that we might describe now as queer. Gay liberation in the 60s and 70s and crucially the Stonewall riots in 1969 were very important for disco.\n", + "\n", + "\n", + " Up until the Stonewall riots, the survival strategy of queer communities was to be as invisible as possible, to not get noticed.\n", + "\n", + "\n", + " Despite the 1960s being characterized as a decade of free love, being gay was still illegal in most of the country. Even in New York City, gay bars didn't allow dancing between same-sex couples. Enter the Stonewall Inn, opened in 1967. Its clientele included drag queens who were often turned away from other gay bars, as well as runaways and homeless youth. And gay men weren't told that they couldn't dance together.\n", + "\n", + "\n", + " In June 1969, Stonewall was raided by the police and the people who went there fought back. The police were actually forced to take cover inside the bar, where the angry mob locked them in and set the building on fire. In the aftermath, the regulations that ruled New York City nightlife relaxed, including the ban on same-sex dancing. Gay nightlife in the city exploded and everyone wanted to dance.\n", + "\n", + "\n", + " Most of the clubs that existed before Stonewall were really very underground, with one or two exceptions. And after that, there was a much more comfortable gathering of people. You were going to celebrate a kind of openness that really hadn't been possible before.\n", + "\n", + "\n", + " That's where the Disco DJ comes in. Disco really gave birth to the modern DJ. These guys were innovating with technology and music in a way that hadn't been done before. And maybe for the first time, they had devoted followings that followed them from club to club.\n", + "\n", + "\n", + " The DJs that were playing what became known as disco were, I think, really adventurous. You could go to a club and hear music that you wouldn't hear anywhere else and hear it put together in a way that you wouldn't hear in any other format.\n", + "\n", + "\n", + " Now, while disco was pretty much defined by gay men, club owners, prominent DJs, producers, record promoters, as it evolved as a genre, it was the voice of female artists, particularly women of color, who came to dominate the sound. And these women were breaking with tradition.\n", + "\n", + "\n", + " LaBelle's Lady Marmalade was an iconic 70s song, unabashedly sexually aggressive, and it was about a sex worker. Literally translates to, do you want to sleep with me? Lady Marmalade happened and I'm really happy and we're happy that it was as big as it was and as big as it was in the clubs and in disco, but that was really just kind of like the icing on the cake of who we were as a group.\n", + "\n", + "\n", + " Four or five years before that track was released, the then-called Patti LaBelle and the Bluebelles traded in their matching dresses and hairstyles for a look that was, well, a little more radical. I think at the time for a girl group to shed their wigs, shed their gowns, shed their, you know, kind of that kind of look and become much more\n", + "\n", + "\n", + " assertive, more vocal, political, social statements. This was really a group of women who were making their own decisions about the music they wanted to write and perform.\n", + "\n", + "\n", + " In her book Hot Stuff, Alice Echols discusses how female disco artists like LaBelle and others expanded the idea of what was socially acceptable for women by singing about female sexual desire. LaBelle was early on the scene with tracks like Lady Marmalade and later Going Down Makes Me Shiver. Then came Donna Summer.\n", + "\n", + "\n", + " although not quite literally, with her 17-minute long moan-a-thon, Love to Love You Baby. And Anita Ward's 1979 hit, Ring My Bell, wasn't actually about ringing any literal bells, a fact that escaped me when I was singing along to it as a kid.\n", + "\n", + "\n", + " Now Hendrix is quick to point out that Black women had been singing about sex and sexuality for a while. That was kind of a part of the, you know, the Black music, that sexual, sensual energy and lyric was always, you know, let's get it on. I don't think it was something that we felt was a taboo. It didn't seem strange or odd to us.\n", + "\n", + "\n", + " But Echols says that the specific focus on female pleasure was something new. The female orgasm became a central theme in a lot of songs, as well as its own sort of instrument, as it was in Love to Love You Baby. It was its own rebuff to the sort of hypermasculinity of rock music that focused on male pleasure. But the explosion of female pleasure didn't really sit well with all the critics.\n", + "\n", + "\n", + " I think the biggest backlash that we received was Lady Mamla because it was everywhere and I think some people who were I guess much more conservative in their musical taste or their outlook on life took exception with the song.\n", + "\n", + "\n", + " Part of the exception to the music was part of a bigger exception to the spread of disco culture and what it represented. Gay nightlife was becoming increasingly cool among celebrities and trendsetters. In a column in 1975, Aletti estimated that 2,000 discos had sprung up across the United States with two or three hundred in New York alone. And that was even before the symbol of disco debauchery Studio 54 opened its doors and Saturday Night Fever broke box office records.\n", + "\n", + "\n", + " Gay clubs, I think, really changed the whole way people went out dancing. And they did represent a kind of freedom to the gay people that went there and a lot of straight people enjoyed going there for the experience of sort of taking part in this liberation.\n", + "\n", + "\n", + " Part of it has to do with the ability to express yourself. And if you can be in a community where you can express yourself truly, then I think people are going to look for it and go to it and want to be there. The spread of disco Zun just bring with it music and dancing. It also brought the evolution of sexual expression and fashion for both gay and straight men.\n", + "\n", + "\n", + " It's especially interesting to look at how disco transformed, or at least tried to transform, what it meant to be a man in the 1970s. The sense that you should be wearing clothes that were much more fitted to the body, much more revealing of the body, that you should be in shape. And it also assumed that you should know how to dance.\n", + "\n", + "\n", + " This became part of what Dennis Altman dubbed in his 1982 book, The Homosexualization of America, with the greatest example of this being the mainstreaming of these guys.\n", + "\n", + "\n", + " By the way, they weren't even marketed as a gay group. Their own label, Casablanca Records, made it clear that they were for clueless straight Americans. Navy officials were so oblivious that they actually allowed the video for In the Navy to be filmed on one of their warships, and they even used it in one of their recruiting videos. But despite mainstream America's embrace of the village people, not everyone was down with how disco was changing and challenging American masculinity.\n", + "\n", + "\n", + " If you compare this to the way that other musical genres in the 70s presented masculinity, so the rock masculinity or country music masculinity, those that were coming out of the disco scene really stood in stark relief. A lot of people resented exactly that impact, the sense that gay culture was sneaking out into the mainstream and taking over on some level.\n", + "\n", + "\n", + " And this really came to a head in 1979. That's when Chicago radio DJ Steve Dahl organized Disco Demolition Night at Comiskey Park, not long after being fired when the station he worked for switched to a disco format. Dahl had this to say when he was asked by news outlets what he had against disco. Well, the first thing I have against it is that I can never find a white three-piece suit that fits me off the rack. So I hate the taste of pina coladas. I'm allergic to gold jewelry, so there's nothing there for me.\n", + "\n", + "\n", + " You can see now, in hindsight, a real concerted effort by journalists, radio personalities and so on to vilify disco ahead of this crash. The disco backlash wasn't just one thing, it was a lot of things coming together. Notably, there was a crash in disco sales, in the sale of disco recordings. The bubble popped, so to speak.\n", + "\n", + "\n", + " But in addition to the backlash and dropping record sales, there was also the impact of the AIDS epidemic, especially on the gay community and the gay community that was going to the clubs. I think the impact that AIDS had on the key audience for a lot of clubs was devastating. You can't keep going when a lot of your audience is dying.\n", + "\n", + "\n", + " A lot of the people who were involved in opening up society, we lost a lot of those people. But disco's legacy lives on, not just through its impact on dance music and club culture, but in the ways that it redefined what it meant to be gay, what it meant to be a woman, what it meant to be a man, and what sex in America was all about.\n", + "\n", + "\n", + " Disco never really died. Disco lived on and went underground back into the queer and especially black and Latinx communities from which it arose. And it turned into things like garage in New York. It turned into house in Chicago. Disco turned into synth pop and Euro disco and all sorts of other things in Europe. Disco had an afterlife in many other parts of the world even after it was supposedly dead. Madonna came up\n", + "\n", + "\n", + " right after this whole death of disco. And if Madonna wasn't disco, I don't know what is. Okay, I can't be the only one who really wants to dance right now. Right? Let us know what your favorite songs are from the era, and don't forget to like, share, and subscribe. And while you're at it, let us know what else you want us to cover here at Pop Americana.\n", + "\n", + "\n", + "\n", + "\n", + "===================\n", + "End of Transcription\n" + ] + } + ], + "source": [ + "# whisper batched transcribe stream example\n", + "data = {\n", + " \"video\": {\n", + " \"path\": \"/nas/jilt/datasets/Documentaries/Crawler/all_audios/Disco Changed Everything. So Why Did It Die-LO_EF1BY6Tg.wav\"\n", + " }, # \"/workspaces/aana_sdk/aana/tests/files/videos/physicsworks.webm\"},\n", + "}\n", + "\n", + "url = \"http://127.0.0.1:8001/video/transcribe_batch\"\n", + "\n", + "response = requests.post(url, data={\"body\": json.dumps(data)}, stream=True)\n", + "\n", + "for chunk in response.iter_content(chunk_size=None):\n", + " json_data = json.loads(chunk)\n", + "\n", + " if \"error\" in json_data:\n", + " print(json_data[\"error\"])\n", + " print(json_data[\"message\"])\n", + " print(json_data[\"stacktrace\"])\n", + " break\n", + "\n", + " segments = json_data.get(\"segments\")\n", + " if segments:\n", + " print(segments[0][\"text\"])\n", + " print(\"\\n\")\n", + " else:\n", + " print(\"\\n\")\n", + " print(\"===================\")\n", + " print(\"End of Transcription\")" + ] } ], "metadata": { From ce1160f3c12f6383ac60d280516b28d3da9f02e6 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Mon, 5 Feb 2024 11:12:21 +0000 Subject: [PATCH 12/88] Added end point for batched transcription --- aana/configs/endpoints.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/aana/configs/endpoints.py b/aana/configs/endpoints.py index 8ff49392..de59005e 100644 --- a/aana/configs/endpoints.py +++ b/aana/configs/endpoints.py @@ -148,8 +148,9 @@ output="video_transcriptions_info_batched_whisper_medium", streaming=True, ), + # TODO: adding id endpoint makes it very slow (>2x) # EndpointOutput( - # name="transcription_id_batched", output="transcription_id_batched" + # name="transcription_id", output="transcription_id_batched" # ), ], streaming=True, From ac2055c0e256240694b3b90043be23755933a025 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Mon, 5 Feb 2024 11:13:52 +0000 Subject: [PATCH 13/88] Added nodes for audio extraction, vad processing, batched transcription and saving batched transcription --- aana/configs/pipeline.py | 69 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 65 insertions(+), 4 deletions(-) diff --git a/aana/configs/pipeline.py b/aana/configs/pipeline.py index 64bbf823..051500e3 100644 --- a/aana/configs/pipeline.py +++ b/aana/configs/pipeline.py @@ -417,6 +417,26 @@ }, ], }, + { + "name": "extract_audio", + "type": "ray_task", + "function": "aana.utils.video.extract_audio", + "dict_output": False, + "inputs": [ + { + "name": "video_object", + "key": "video_input", + "path": "video.video", + }, + ], + "outputs": [ + { + "name": "audio_object", + "key": "output", + "path": "video.audio", + }, + ], + }, { "name": "generate_frames_for_video", "type": "ray_task", @@ -538,9 +558,9 @@ "method": "asr_preprocess_vad", "inputs": [ { - "name": "video_object", + "name": "audio_object", "key": "media", - "path": "video.video", + "path": "video.audio", }, { "name": "vad_params", @@ -566,9 +586,9 @@ "method": "batched_inference", "inputs": [ { - "name": "video_object", + "name": "audio_object", "key": "media", - "path": "video.video", + "path": "video.audio", }, { "name": "video_transcriptions_vad_segments", @@ -603,6 +623,47 @@ }, ], }, + { + "name": "whisper_medium_batched_inference_full_video", + "type": "ray_deployment", + "deployment_name": "whisper_deployment_medium", + "data_type": "generator", + "generator_path": "video", + "method": "batched_inference_full", + "inputs": [ + { + "name": "video_object", + "key": "media", + "path": "video.video", + }, + { + "name": "whisper_params", + "key": "params", + "path": "video_batch.whisper_params", + "data_model": WhisperParams, + }, + ], + "outputs": [ + { + "name": "video_transcriptions_segments_batched_full_whisper_medium", + "key": "segments", + "path": "video.segments_batched_full", + "data_model": AsrSegments, + }, + { + "name": "video_transcriptions_info_batched_full_whisper_medium", + "key": "transcription_info", + "path": "video.transcription_info_batched_full", + "data_model": AsrTranscriptionInfo, + }, + { + "name": "video_transcriptions_batched_full_whisper_medium", + "key": "transcription", + "path": "video.transcription_batched_full", + "data_model": AsrTranscription, + }, + ], + }, { "name": "media_id", "type": "input", From f5d40a419a5ddce6a88353ada878ef476519db90 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Mon, 5 Feb 2024 11:15:05 +0000 Subject: [PATCH 14/88] Modified deployment for faster audio reading --- aana/deployments/vad_deployment.py | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/aana/deployments/vad_deployment.py b/aana/deployments/vad_deployment.py index 20b4c111..3c7d6530 100644 --- a/aana/deployments/vad_deployment.py +++ b/aana/deployments/vad_deployment.py @@ -24,7 +24,6 @@ from aana.models.pydantic.time_interval import TimeInterval from ray import serve -from faster_whisper.audio import decode_audio from pydantic import BaseModel, Field @@ -301,8 +300,6 @@ def download_vad_model(model_fp, vad_segmentation_url): config_obj = VadParams(**config) self.device = "cuda" if torch.cuda.is_available() else "cpu" - # print(config_obj.model_fp) - # print(config_obj.vad_segmentation_url) filepath = download_vad_model( config_obj.model_fp, config_obj.vad_segmentation_url ) @@ -321,7 +318,7 @@ def download_vad_model(model_fp, vad_segmentation_url): ) self.vad_pipeline.instantiate(hyperparameters) - # OPTIONAL: audio loader function if asr model is not used in deployment.:can not use subprocess in an async function: use whisper ftb + # OPTIONAL: audio loader function if asr model is not used in deployment. async def load_audio(self, file: str, sr: int = 16000): """Open an audio file and read as mono waveform, resampling as necessary. @@ -373,8 +370,6 @@ async def merge_chunks( Returns: merged_segments list(VadSegment): The list of segments. - Raises: - InferenceException: If the inference fails. """ chunk_size = params["chunk_size"] onset = params["merge_onset"] @@ -400,7 +395,7 @@ async def merge_chunks( ) # To account for edge errors additional 100ms padding if len(segments_list) == 0: - print("No active speech found in audio") + # No active speech found in audio. return [] # Make sure the starting point is the start of the segment. @@ -436,14 +431,23 @@ async def vad_inference(self, media: Video) -> list[dict]: media (Video): The media to perform vad. Returns: - vad_segments (VadSegment): The list of vad segments. + vad_segments (list[dict]): The list of vad segments. Raises: InferenceException: If the vad inference fails. """ media_path: str = str(media.path) - audio_array = decode_audio(media_path) # self.load_audio() has subprocess issue - # TODO: raise valueError if not able to load + + # load audio data + audio_array = ( + np.frombuffer(open(media_path, "rb").read(), dtype=np.int16) + .flatten() + .astype(np.float32) + / 32768.0 + ) + # OPTIONAL: Use load_audio function if audio extraction is not performed. + # audio_array = self.load_audio(media_path) + try: vad_segments = self.vad_pipeline( { From 60c31ac4771b55f23971be797234bb094ff16019 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Mon, 5 Feb 2024 11:16:27 +0000 Subject: [PATCH 15/88] Modified deployment for faster audio reading and handling empty vad segments --- aana/deployments/whisper_deployment.py | 80 +++++++++++++++++--------- 1 file changed, 53 insertions(+), 27 deletions(-) diff --git a/aana/deployments/whisper_deployment.py b/aana/deployments/whisper_deployment.py index 4aa9bdcd..6e9f0784 100644 --- a/aana/deployments/whisper_deployment.py +++ b/aana/deployments/whisper_deployment.py @@ -170,8 +170,15 @@ async def transcribe( if not params: params = WhisperParams() media_path: str = str(media.path) + # load from buffer and pass it + audio_array = ( + np.frombuffer(open(media_path, "rb").read(), dtype=np.int16) + .flatten() + .astype(np.float32) + / 32768.0 + ) try: - segments, info = self.model.transcribe(media_path, **params.dict()) + segments, info = self.model.transcribe(audio_array, **params.dict()) except Exception as e: raise InferenceException(self.model_name) from e @@ -305,35 +312,54 @@ async def batched_inference( ) media_path: str = str(media.path) - audio_data = decode_audio(media_path) + + # OPTIONAL: Use faster_whisper function to load media or use from buffer for loading extracted audio + # audio_data = decode_audio(media_path) # TODO: raise value error if not able to load the audio - # dict_format = kp['vad_segments'][0].to_dict() - vad_input = [seg.to_dict() for seg in vad_segments] - # print(vad_input[0]) - try: - result = self.batched_model.transcribe_stream( - audio_data, - vad_segments=vad_input, - batch_size=batch_size, - ) - except Exception as e: - raise InferenceException(self.model_name) from e + audio_array = ( + np.frombuffer(open(media_path, "rb").read(), dtype=np.int16) + .flatten() + .astype(np.float32) + / 32768.0 + ) - count = 0 - for segment, info in result: - if count == 0: - # converting info dictionary to an object: - transcription_object = SimpleNamespace(**info) - asr_transcription_info = AsrTranscriptionInfo.from_whisper( - transcription_object - ) - count += 1 - asr_segments = [AsrSegment.from_whisper(segment)] - asr_transcription = AsrTranscription(text=segment.text) + vad_input = [seg.to_dict() for seg in vad_segments] + if not vad_input: + # return empty output with language as silence for silent audios + info = {"language": "silence", "language_probability": 1.0} yield WhisperOutput( - segments=asr_segments, - transcription_info=asr_transcription_info, - transcription=asr_transcription, + segments=[], + transcription_info=AsrTranscriptionInfo.from_whisper( + SimpleNamespace(**info) + ), + transcription=AsrTranscription(text=""), ) + else: + try: + result = self.batched_model.transcribe_stream( + audio_array, + vad_segments=vad_input, + batch_size=batch_size, + ) + except Exception as e: + raise InferenceException(self.model_name) from e + + # count = 0 + for count, (segment, info) in enumerate(result): + if count == 0: + # converting info dictionary to an object: + transcription_object = SimpleNamespace(**info) + asr_transcription_info = AsrTranscriptionInfo.from_whisper( + transcription_object + ) + # count += 1 + asr_segments = [AsrSegment.from_whisper(segment)] + asr_transcription = AsrTranscription(text=segment.text) + + yield WhisperOutput( + segments=asr_segments, + transcription_info=asr_transcription_info, + transcription=asr_transcription, + ) From aa6a33ada20c323abcb80454cf90453f83962d42 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Mon, 5 Feb 2024 11:18:45 +0000 Subject: [PATCH 16/88] Added extract_audio function utility for video to audio conversion --- aana/utils/video.py | 76 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 75 insertions(+), 1 deletion(-) diff --git a/aana/utils/video.py b/aana/utils/video.py index 1a897aa3..7134794e 100644 --- a/aana/utils/video.py +++ b/aana/utils/video.py @@ -5,7 +5,8 @@ from math import floor from pathlib import Path from typing import TypedDict - +import subprocess +import wave import numpy as np import torch, decord # noqa: F401 # See https://github.com/dmlc/decord/issues/263 from decord import DECORDError @@ -210,6 +211,79 @@ def download_video(video_input: VideoInput | Video) -> Video: return video_input.convert_input_to_object() +def load_audio(file: str, sr: int = 16000): + """Open an audio file and read as mono waveform, resampling as necessary. + + Args: + file (str): The audio file to open. + sr (int): The sample rate to resample the audio if necessary. + + Returns: + A NumPy array containing the audio waveform, in float32 dtype. + + Raises: + RuntimeError: if ffmpeg fails to convert and load the audio. + """ + try: + # Launches a subprocess to decode audio while down-mixing and resampling as necessary. + # Requires the ffmpeg CLI to be installed. + cmd = [ + "ffmpeg", + "-nostdin", + "-threads", + "0", + "-i", + file, + "-f", + "s16le", + "-ac", + "1", + "-acodec", + "pcm_s16le", + "-ar", + str(sr), + "-", + ] + out = subprocess.run(cmd, capture_output=True, check=True).stdout + except subprocess.CalledProcessError as e: + raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e + + # Save audio to a WAV file + wav_file_path = Path(file).with_suffix(".wav") + with wave.open(str(wav_file_path), "wb") as wav_file: + wav_file.setnchannels(1) # Mono audio + wav_file.setsampwidth(2) # 16-bit audio + wav_file.setframerate(16000) # Sample rate + wav_file.writeframes(out) + + return wav_file_path + + +def extract_audio(video_input: VideoInput | Video) -> Video: + """Extract the audio file from a VideoInput and return as a Video object. + + Args: + video_input (VideoInput): The media file to extract audio. + + Returns: + A Video object containing the extracted audio path. + + """ + media_path = str(video_input.path) + if not media_path.endswith(".wav"): + audio_raw = load_audio(media_path) + else: + audio_raw = video_input.path + + return Video( + path=audio_raw, + url=video_input.url, + media_id=video_input.media_id, + title=video_input.title, + description=video_input.description, + ) + + def generate_combined_timeline( transcription_segments: AsrSegments, captions: list[str], From de6d00ab1d7253b1c028ab6f7a9f1ca91b647f22 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Mon, 5 Feb 2024 11:19:41 +0000 Subject: [PATCH 17/88] Added examples and benchmarking scripts on documentary data --- notebooks/demo.ipynb | 226 +++++++++++++++++++++++++------------------ 1 file changed, 133 insertions(+), 93 deletions(-) diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb index b18d59b9..4b0d13fc 100644 --- a/notebooks/demo.ipynb +++ b/notebooks/demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -160,113 +160,32 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " This is one of my favorite opening scenes to a movie. It's from 1977's Saturday Night Fever. Tony Manero's walk, the Bee Gees in the background, the paint can, double stacked pizza. This.\n", - "\n", - "\n", - " I love this scene because there's this weird sense of glamour to everything. And that's actually pretty reflective of the moment in history this film tries to capture. A moment in the 70s that left a lasting impact on American sexuality, especially the American man, and prompted a backlash.\n", - "\n", - "\n", - " That beat is known as Four on the Floor, created by drummer Earl Young, and it changed the way we danced. And the genesis of the beat was, according to Young in a BBC interview, something kind of mundane. It's just like a walking move like this. It's a walking feel. So when people hear that, you automatically want to move something. And I say, well, gee, this is pretty cool. So I said, I'm going to put this to a groove.\n", - "\n", - "\n", - " Now here's Young playing with the tramps on a classic disco hit that you probably will recognize. That's the sound that most people would categorize as being disco. But the type of music people were dancing to in actual discos in the 70s didn't always fit that description.\n", - "\n", - "\n", - " Disco was a very eclectic sort of music. It started out being a little bit of everything, and I think at its best it still remains very, very unpredictable. And it was those at the margins of society who really defined the direction that disco would take.\n", - "\n", - "\n", - " The venues where disco flourished and first took root and the communities that fed into that and that served as its dancers, as its music producers, as its DJs and so on came directly out of especially black and brown, Latinx communities that we might describe now as queer. Gay liberation in the 60s and 70s and crucially the Stonewall riots in 1969 were very important for disco.\n", - "\n", - "\n", - " Up until the Stonewall riots, the survival strategy of queer communities was to be as invisible as possible, to not get noticed.\n", - "\n", - "\n", - " Despite the 1960s being characterized as a decade of free love, being gay was still illegal in most of the country. Even in New York City, gay bars didn't allow dancing between same-sex couples. Enter the Stonewall Inn, opened in 1967. Its clientele included drag queens who were often turned away from other gay bars, as well as runaways and homeless youth. And gay men weren't told that they couldn't dance together.\n", - "\n", - "\n", - " In June 1969, Stonewall was raided by the police and the people who went there fought back. The police were actually forced to take cover inside the bar, where the angry mob locked them in and set the building on fire. In the aftermath, the regulations that ruled New York City nightlife relaxed, including the ban on same-sex dancing. Gay nightlife in the city exploded and everyone wanted to dance.\n", - "\n", - "\n", - " Most of the clubs that existed before Stonewall were really very underground, with one or two exceptions. And after that, there was a much more comfortable gathering of people. You were going to celebrate a kind of openness that really hadn't been possible before.\n", - "\n", - "\n", - " That's where the Disco DJ comes in. Disco really gave birth to the modern DJ. These guys were innovating with technology and music in a way that hadn't been done before. And maybe for the first time, they had devoted followings that followed them from club to club.\n", - "\n", - "\n", - " The DJs that were playing what became known as disco were, I think, really adventurous. You could go to a club and hear music that you wouldn't hear anywhere else and hear it put together in a way that you wouldn't hear in any other format.\n", - "\n", - "\n", - " Now, while disco was pretty much defined by gay men, club owners, prominent DJs, producers, record promoters, as it evolved as a genre, it was the voice of female artists, particularly women of color, who came to dominate the sound. And these women were breaking with tradition.\n", - "\n", - "\n", - " LaBelle's Lady Marmalade was an iconic 70s song, unabashedly sexually aggressive, and it was about a sex worker. Literally translates to, do you want to sleep with me? Lady Marmalade happened and I'm really happy and we're happy that it was as big as it was and as big as it was in the clubs and in disco, but that was really just kind of like the icing on the cake of who we were as a group.\n", - "\n", - "\n", - " Four or five years before that track was released, the then-called Patti LaBelle and the Bluebelles traded in their matching dresses and hairstyles for a look that was, well, a little more radical. I think at the time for a girl group to shed their wigs, shed their gowns, shed their, you know, kind of that kind of look and become much more\n", - "\n", - "\n", - " assertive, more vocal, political, social statements. This was really a group of women who were making their own decisions about the music they wanted to write and perform.\n", - "\n", - "\n", - " In her book Hot Stuff, Alice Echols discusses how female disco artists like LaBelle and others expanded the idea of what was socially acceptable for women by singing about female sexual desire. LaBelle was early on the scene with tracks like Lady Marmalade and later Going Down Makes Me Shiver. Then came Donna Summer.\n", - "\n", - "\n", - " although not quite literally, with her 17-minute long moan-a-thon, Love to Love You Baby. And Anita Ward's 1979 hit, Ring My Bell, wasn't actually about ringing any literal bells, a fact that escaped me when I was singing along to it as a kid.\n", - "\n", - "\n", - " Now Hendrix is quick to point out that Black women had been singing about sex and sexuality for a while. That was kind of a part of the, you know, the Black music, that sexual, sensual energy and lyric was always, you know, let's get it on. I don't think it was something that we felt was a taboo. It didn't seem strange or odd to us.\n", - "\n", - "\n", - " But Echols says that the specific focus on female pleasure was something new. The female orgasm became a central theme in a lot of songs, as well as its own sort of instrument, as it was in Love to Love You Baby. It was its own rebuff to the sort of hypermasculinity of rock music that focused on male pleasure. But the explosion of female pleasure didn't really sit well with all the critics.\n", - "\n", - "\n", - " I think the biggest backlash that we received was Lady Mamla because it was everywhere and I think some people who were I guess much more conservative in their musical taste or their outlook on life took exception with the song.\n", - "\n", - "\n", - " Part of the exception to the music was part of a bigger exception to the spread of disco culture and what it represented. Gay nightlife was becoming increasingly cool among celebrities and trendsetters. In a column in 1975, Aletti estimated that 2,000 discos had sprung up across the United States with two or three hundred in New York alone. And that was even before the symbol of disco debauchery Studio 54 opened its doors and Saturday Night Fever broke box office records.\n", - "\n", - "\n", - " Gay clubs, I think, really changed the whole way people went out dancing. And they did represent a kind of freedom to the gay people that went there and a lot of straight people enjoyed going there for the experience of sort of taking part in this liberation.\n", - "\n", - "\n", - " Part of it has to do with the ability to express yourself. And if you can be in a community where you can express yourself truly, then I think people are going to look for it and go to it and want to be there. The spread of disco Zun just bring with it music and dancing. It also brought the evolution of sexual expression and fashion for both gay and straight men.\n", - "\n", - "\n", - " It's especially interesting to look at how disco transformed, or at least tried to transform, what it meant to be a man in the 1970s. The sense that you should be wearing clothes that were much more fitted to the body, much more revealing of the body, that you should be in shape. And it also assumed that you should know how to dance.\n", - "\n", - "\n", - " This became part of what Dennis Altman dubbed in his 1982 book, The Homosexualization of America, with the greatest example of this being the mainstreaming of these guys.\n", - "\n", + " Now I want to return to the conservation of mechanical energy. I have here a pendulum. I have an object that weighs 15 kilograms and I can lift it up one meter, which I have done now. That means I've done work. mgh is the work I have done, believe me. I've increased the potential energy of this object. 15 times 10 is about 150 joules.\n", "\n", - " By the way, they weren't even marketed as a gay group. Their own label, Casablanca Records, made it clear that they were for clueless straight Americans. Navy officials were so oblivious that they actually allowed the video for In the Navy to be filmed on one of their warships, and they even used it in one of their recruiting videos. But despite mainstream America's embrace of the village people, not everyone was down with how disco was changing and challenging American masculinity.\n", "\n", + " then that will be converted to kinetic energy. If I would let it swing from one meter height and you would be there and it would hit you, you'd be dead. 150 joules is enough to kill you. They use these devices. They're called a racquetball. They use them to demolish buildings. You lift up a very heavy object, even heavier than this\n", "\n", - " If you compare this to the way that other musical genres in the 70s presented masculinity, so the rock masculinity or country music masculinity, those that were coming out of the disco scene really stood in stark relief. A lot of people resented exactly that impact, the sense that gay culture was sneaking out into the mainstream and taking over on some level.\n", "\n", + " And then you let it go, you swing it, thereby converting gravitational potential energy into kinetic energy, and that way you can demolish a building. You just let it hit... and it breaks a building. And that's the whole idea of wrecking. So you are using, then, the conversion of gravitational potential energy to kinetic energy.\n", "\n", - " And this really came to a head in 1979. That's when Chicago radio DJ Steve Dahl organized Disco Demolition Night at Comiskey Park, not long after being fired when the station he worked for switched to a disco format. Dahl had this to say when he was asked by news outlets what he had against disco. Well, the first thing I have against it is that I can never find a white three-piece suit that fits me off the rack. So I hate the taste of pina coladas. I'm allergic to gold jewelry, so there's nothing there for me.\n", "\n", + " Now, I am such a strong believer of the conservation of mechanical energy that I am willing to put my life on the line. If I release that bob from a certain height then that bob can never come back to a point where the height is any larger.\n", "\n", - " You can see now, in hindsight, a real concerted effort by journalists, radio personalities and so on to vilify disco ahead of this crash. The disco backlash wasn't just one thing, it was a lot of things coming together. Notably, there was a crash in disco sales, in the sale of disco recordings. The bubble popped, so to speak.\n", "\n", + " and it swings, then when it reaches here it could not be higher. There is a conversion from gravitational potential energy to kinetic energy back to gravitational potential energy and it will come to a stop here. And when it swings back, it should not be able to reach any higher, provided that I do not give this object an initial speed when I stand here. I trust the conservation of mechanical energy\n", "\n", - " But in addition to the backlash and dropping record sales, there was also the impact of the AIDS epidemic, especially on the gay community and the gay community that was going to the clubs. I think the impact that AIDS had on the key audience for a lot of clubs was devastating. You can't keep going when a lot of your audience is dying.\n", "\n", + " for 100%. I may not trust myself. I'm going to release this object and I hope I will be able to do it at zero speed so that when it comes back it may touch my chin but it may not crush my chin. I want you to be extremely quiet because this is no joke. If I don't succeed in giving it zero speed\n", "\n", - " A lot of the people who were involved in opening up society, we lost a lot of those people. But disco's legacy lives on, not just through its impact on dance music and club culture, but in the ways that it redefined what it meant to be gay, what it meant to be a woman, what it meant to be a man, and what sex in America was all about.\n", "\n", - "\n", - " Disco never really died. Disco lived on and went underground back into the queer and especially black and Latinx communities from which it arose. And it turned into things like garage in New York. It turned into house in Chicago. Disco turned into synth pop and Euro disco and all sorts of other things in Europe. Disco had an afterlife in many other parts of the world even after it was supposedly dead. Madonna came up\n", - "\n", - "\n", - " right after this whole death of disco. And if Madonna wasn't disco, I don't know what is. Okay, I can't be the only one who really wants to dance right now. Right? Let us know what your favorite songs are from the era, and don't forget to like, share, and subscribe. And while you're at it, let us know what else you want us to cover here at Pop Americana.\n", + " I will close my eyes. I don't want to see this. So please be very quiet. I almost didn't sleep all night. Three, two, one, zero. Physics works and I'm still alive.\n", "\n", "\n", "\n", @@ -277,11 +196,13 @@ } ], "source": [ + "import requests, json\n", + "\n", "# whisper batched transcribe stream example\n", "data = {\n", " \"video\": {\n", - " \"path\": \"/nas/jilt/datasets/Documentaries/Crawler/all_audios/Disco Changed Everything. So Why Did It Die-LO_EF1BY6Tg.wav\"\n", - " }, # \"/workspaces/aana_sdk/aana/tests/files/videos/physicsworks.webm\"},\n", + " \"path\": \"/workspaces/aana_sdk/aana/tests/files/videos/physicsworks.webm\" # \"/workspaces/aana_sdk/aana/tests/files/videos/squirrel.mp4\"\n", + " },\n", "}\n", "\n", "url = \"http://127.0.0.1:8001/video/transcribe_batch\"\n", @@ -306,6 +227,125 @@ " print(\"===================\")\n", " print(\"End of Transcription\")" ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[99.17471241950989, 99.85424876213074, 98.04211807250977, 98.51013016700745, 99.3893518447876, 98.87187552452087, 98.49910855293274, 99.37014293670654, 97.59414887428284, 98.30523371696472]\n" + ] + } + ], + "source": [ + "# transcribe sequential benchmarking from /nas dataset\n", + "import os, requests, json\n", + "import time\n", + "\n", + "# documentary videos\n", + "url = \"http://127.0.0.1:8001/video/transcribe_batch\"\n", + "base_dir = \"/nas/jilt/datasets/Documentaries/Crawler/all_videos\"\n", + "\n", + "paths = [\n", + " os.path.join(base_dir, pth) for pth in os.listdir(base_dir) if pth.endswith(\".mp4\")\n", + "]\n", + "difference = []\n", + "for i in range(10):\n", + " start_time = time.time()\n", + " for path in paths:\n", + " data = {\n", + " \"video\": {\"path\": path},\n", + " }\n", + " response = requests.post(url, data={\"body\": json.dumps(data)}, stream=True)\n", + "\n", + " for chunk in response.iter_content(chunk_size=None):\n", + " json_data = json.loads(chunk)\n", + "\n", + " if \"error\" in json_data:\n", + " print(json_data[\"error\"])\n", + " print(json_data[\"message\"])\n", + " print(json_data[\"stacktrace\"])\n", + " break\n", + "\n", + " segments = json_data.get(\"segments\")\n", + "\n", + " differ = time.time() - start_time\n", + " difference.append(differ)\n", + "print(difference)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[45.32583928108215, 47.194429874420166, 44.753589391708374, 45.29305362701416, 44.94037747383118, 44.15987682342529, 43.98680377006531, 49.56646513938904, 43.43703770637512, 44.23016595840454]\n" + ] + } + ], + "source": [ + "# transcribe threading benchmarking based on /nas dataset\n", + "import os, requests, json\n", + "import time\n", + "import threading\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "# documentary videos\n", + "url = \"http://127.0.0.1:8001/video/transcribe_batch\"\n", + "base_dir = \"/nas/jilt/datasets/Documentaries/Crawler/all_videos\"\n", + "\n", + "paths = [\n", + " os.path.join(base_dir, pth) for pth in os.listdir(base_dir) if pth.endswith(\".mp4\")\n", + "]\n", + "\n", + "difference = []\n", + "\n", + "\n", + "def predict_on_video(path):\n", + " data = {\n", + " \"video\": {\"path\": path},\n", + " }\n", + " response = requests.post(url, data={\"body\": json.dumps(data)}, stream=True)\n", + " op_text = []\n", + " for chunk in response.iter_content(chunk_size=None):\n", + " json_data = json.loads(chunk)\n", + " if \"error\" in json_data:\n", + " print(json_data[\"error\"])\n", + " print(json_data[\"message\"])\n", + " print(json_data[\"stacktrace\"])\n", + " break\n", + "\n", + " segments = json_data.get(\"segments\")\n", + " if segments:\n", + " op_text.append(segments[0][\"text\"])\n", + " else:\n", + " return op_text\n", + "\n", + "\n", + "# now do it for 20 iterations\n", + "difference = []\n", + "for i in range(10):\n", + " start_time = time.time()\n", + " with ThreadPoolExecutor(max_workers=10) as executor:\n", + "\n", + " def worker_func(path):\n", + " return predict_on_video(path)\n", + "\n", + " results = list(\n", + " zip(paths, executor.map(worker_func, paths))\n", + " ) # should get a list of lists.\n", + " differ = time.time() - start_time\n", + " difference.append(differ)\n", + "print(difference)" + ] } ], "metadata": { From f86016685113e6d8b50da6867721f528ffad19a8 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Mon, 5 Feb 2024 11:22:56 +0000 Subject: [PATCH 18/88] test script for vad_deployment --- aana/tests/deployments/test_vad_deployment.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 aana/tests/deployments/test_vad_deployment.py diff --git a/aana/tests/deployments/test_vad_deployment.py b/aana/tests/deployments/test_vad_deployment.py new file mode 100644 index 00000000..e69de29b From 5c697d74558a565883f6a3660d9db77820dd3c6c Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:39:01 +0000 Subject: [PATCH 19/88] changes to vad deployment --- aana/configs/deployments.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/aana/configs/deployments.py b/aana/configs/deployments.py index d2a266bf..9c7dba32 100644 --- a/aana/configs/deployments.py +++ b/aana/configs/deployments.py @@ -1,4 +1,5 @@ from aana.deployments.hf_blip2_deployment import HFBlip2Config, HFBlip2Deployment +from aana.deployments.vad_deployment import VadConfig, VadDeployment from aana.deployments.vllm_deployment import VLLMConfig, VLLMDeployment from aana.deployments.whisper_deployment import ( WhisperComputeType, @@ -6,8 +7,6 @@ WhisperDeployment, WhisperModelSize, ) -from aana.deployments.vad_deployment import VadDeployment, VadConfig - from aana.models.core.dtype import Dtype from aana.models.pydantic.sampling_params import SamplingParams From 25a97a730db6cf120068c3b866fe6e2dd8d7ec0d Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:39:34 +0000 Subject: [PATCH 20/88] batched_inference endpoint --- aana/configs/endpoints.py | 43 +++++++++++++++++---------------------- 1 file changed, 19 insertions(+), 24 deletions(-) diff --git a/aana/configs/endpoints.py b/aana/configs/endpoints.py index de59005e..01eb8ab6 100644 --- a/aana/configs/endpoints.py +++ b/aana/configs/endpoints.py @@ -88,17 +88,17 @@ outputs=[ EndpointOutput( name="transcription", - output="video_transcriptions_whisper_medium", + output="audio_transcriptions_whisper_medium", streaming=True, ), EndpointOutput( name="segments", - output="video_transcriptions_segments_whisper_medium", + output="audio_transcriptions_segments_whisper_medium", streaming=True, ), EndpointOutput( name="info", - output="video_transcriptions_info_whisper_medium", + output="audio_transcriptions_info_whisper_medium", streaming=True, ), EndpointOutput(name="transcription_id", output="transcription_id"), @@ -112,15 +112,15 @@ outputs=[ EndpointOutput( name="transcription", - output="video_transcriptions_whisper_medium_from_db", + output="audio_transcriptions_whisper_medium_from_db", ), EndpointOutput( name="segments", - output="video_transcriptions_segments_whisper_medium_from_db", + output="audio_transcriptions_segments_whisper_medium_from_db", ), EndpointOutput( name="info", - output="video_transcriptions_info_whisper_medium_from_db", + output="audio_transcriptions_info_whisper_medium_from_db", ), ], ), @@ -129,29 +129,24 @@ path="/video/transcribe_batch", summary="Transcribe a video using Whisper Medium in batch mode", outputs=[ - EndpointOutput( - name="vad_segments", - output="video_transcriptions_vad_segments", - ), EndpointOutput( name="transcription", - output="video_transcriptions_batched_whisper_medium", + output="audio_transcriptions_batched_whisper_medium", streaming=True, ), EndpointOutput( name="segments", - output="video_transcriptions_segments_batched_whisper_medium", + output="audio_transcriptions_segments_batched_whisper_medium", streaming=True, ), EndpointOutput( name="info", - output="video_transcriptions_info_batched_whisper_medium", + output="audio_transcriptions_info_batched_whisper_medium", streaming=True, ), - # TODO: adding id endpoint makes it very slow (>2x) - # EndpointOutput( - # name="transcription_id", output="transcription_id_batched" - # ), + EndpointOutput( + name="transcription_id", output="transcription_id_batched" + ), ], streaming=True, ), @@ -198,17 +193,17 @@ outputs=[ EndpointOutput( name="transcription", - output="video_transcriptions_whisper_medium", + output="audio_transcriptions_whisper_medium", streaming=True, ), EndpointOutput( name="segments", - output="video_transcriptions_segments_whisper_medium", + output="audio_transcriptions_segments_whisper_medium", streaming=True, ), EndpointOutput( name="info", - output="video_transcriptions_info_whisper_medium", + output="audio_transcriptions_info_whisper_medium", streaming=True, ), EndpointOutput(name="transcription_id", output="transcription_id"), @@ -222,22 +217,22 @@ outputs=[ EndpointOutput( name="transcription", - output="video_transcriptions_whisper_medium", + output="audio_transcriptions_whisper_medium", streaming=True, ), EndpointOutput( name="segments", - output="video_transcriptions_segments_whisper_medium", + output="audio_transcriptions_segments_whisper_medium", streaming=True, ), EndpointOutput( name="info", - output="video_transcriptions_info_whisper_medium", + output="audio_transcriptions_info_whisper_medium", streaming=True, ), EndpointOutput( name="captions", - output="video_captions_hf_blip2_opt_2_7b", + output="audio_captions_hf_blip2_opt_2_7b", streaming=True, ), EndpointOutput( From 90d41de03f3d984d0554df5368f1a1a07d1b76f1 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:39:54 +0000 Subject: [PATCH 21/88] pipeline changes for Audio dataclass --- aana/configs/pipeline.py | 147 ++++++++++++++------------------------- 1 file changed, 53 insertions(+), 94 deletions(-) diff --git a/aana/configs/pipeline.py b/aana/configs/pipeline.py index 051500e3..7fa95a03 100644 --- a/aana/configs/pipeline.py +++ b/aana/configs/pipeline.py @@ -285,7 +285,7 @@ "key": "video", "path": "video_batch.videos.[*].video", }, - {"name": "video_params", "key": "params", "path": "video_batch.params"}, + {"name": "video_params", "key": "params", "path": "-params"}, ], "outputs": [ { @@ -421,7 +421,7 @@ "name": "extract_audio", "type": "ray_task", "function": "aana.utils.video.extract_audio", - "dict_output": False, + "dict_output": False, # TODO:check "inputs": [ { "name": "video_object", @@ -433,7 +433,7 @@ { "name": "audio_object", "key": "output", - "path": "video.audio", + "path": "audio.audio", }, ], }, @@ -498,7 +498,7 @@ ], }, { - "name": "whisper_medium_transcribe_video", + "name": "whisper_medium_transcribe_audio", "type": "ray_deployment", "deployment_name": "whisper_deployment_medium", "data_type": "generator", @@ -506,9 +506,9 @@ "method": "transcribe_stream", "inputs": [ { - "name": "video_object", + "name": "audio_object", "key": "media", - "path": "video.video", + "path": "audio.audio", }, { "name": "whisper_params", @@ -519,21 +519,21 @@ ], "outputs": [ { - "name": "video_transcriptions_segments_whisper_medium", + "name": "audio_transcriptions_segments_whisper_medium", "key": "segments", - "path": "video.segments", + "path": "audio.segments", "data_model": AsrSegments, }, { - "name": "video_transcriptions_info_whisper_medium", + "name": "audio_transcriptions_info_whisper_medium", "key": "transcription_info", - "path": "video.transcription_info", + "path": "audio.transcription_info", "data_model": AsrTranscriptionInfo, }, { - "name": "video_transcriptions_whisper_medium", + "name": "audio_transcriptions_whisper_medium", "key": "transcription", - "path": "video.transcription", + "path": "audio.transcription", "data_model": AsrTranscription, }, ], @@ -546,13 +546,13 @@ { "name": "vad_params", "key": "vad_params", - "path": "video_batch.vad_params", + "path": "audio.vad_params", "data_model": VadParams, } ], }, { - "name": "vad_batched_inference_video", + "name": "vad_batched_inference_audio", "type": "ray_deployment", "deployment_name": "vad_deployment", "method": "asr_preprocess_vad", @@ -560,25 +560,25 @@ { "name": "audio_object", "key": "media", - "path": "video.audio", + "path": "audio.audio", }, { "name": "vad_params", "key": "params", - "path": "video_batch.vad_params", + "path": "audio.vad_params", }, ], "outputs": [ { - "name": "video_transcriptions_vad_segments", + "name": "audio_transcriptions_vad_segments", "key": "vad_segments", - "path": "video.vad_segments", + "path": "audio.vad_segments", "data_model": VadSegments, }, ], }, { - "name": "whisper_medium_batched_inference_video", + "name": "whisper_medium_batched_inference_audio", "type": "ray_deployment", "deployment_name": "whisper_deployment_medium", "data_type": "generator", @@ -588,53 +588,12 @@ { "name": "audio_object", "key": "media", - "path": "video.audio", + "path": "audio.audio", }, { - "name": "video_transcriptions_vad_segments", + "name": "audio_transcriptions_vad_segments", "key": "vad_segments", - "path": "video.vad_segments", - }, - { - "name": "whisper_params", - "key": "params", - "path": "video_batch.whisper_params", - "data_model": WhisperParams, - }, - ], - "outputs": [ - { - "name": "video_transcriptions_segments_batched_whisper_medium", - "key": "segments", - "path": "video.segments_batched", - "data_model": AsrSegments, - }, - { - "name": "video_transcriptions_info_batched_whisper_medium", - "key": "transcription_info", - "path": "video.transcription_info_batched", - "data_model": AsrTranscriptionInfo, - }, - { - "name": "video_transcriptions_batched_whisper_medium", - "key": "transcription", - "path": "video.transcription_batched", - "data_model": AsrTranscription, - }, - ], - }, - { - "name": "whisper_medium_batched_inference_full_video", - "type": "ray_deployment", - "deployment_name": "whisper_deployment_medium", - "data_type": "generator", - "generator_path": "video", - "method": "batched_inference_full", - "inputs": [ - { - "name": "video_object", - "key": "media", - "path": "video.video", + "path": "audio.vad_segments", }, { "name": "whisper_params", @@ -645,21 +604,21 @@ ], "outputs": [ { - "name": "video_transcriptions_segments_batched_full_whisper_medium", + "name": "audio_transcriptions_segments_batched_whisper_medium", "key": "segments", - "path": "video.segments_batched_full", + "path": "audio.segments_batched", "data_model": AsrSegments, }, { - "name": "video_transcriptions_info_batched_full_whisper_medium", + "name": "audio_transcriptions_info_batched_whisper_medium", "key": "transcription_info", - "path": "video.transcription_info_batched_full", + "path": "audio.transcription_info_batched", "data_model": AsrTranscriptionInfo, }, { - "name": "video_transcriptions_batched_full_whisper_medium", + "name": "audio_transcriptions_batched_whisper_medium", "key": "transcription", - "path": "video.transcription_batched_full", + "path": "audio.transcription_batched", "data_model": AsrTranscription, }, ], @@ -710,7 +669,7 @@ ], }, { - "name": "load_video_transcription", + "name": "load_audio_transcription", "type": "function", "function": "aana.utils.db.load_video_transcription", "dict_output": True, @@ -726,21 +685,21 @@ ], "outputs": [ { - "name": "video_transcriptions_info_whisper_medium_from_db", + "name": "audio_transcriptions_info_whisper_medium_from_db", "key": "transcription_info", - "path": "video_transcriptions_info_whisper_medium_from_db", + "path": "audio_transcriptions_info_whisper_medium_from_db", "data_model": AsrTranscriptionInfo, }, { - "name": "video_transcriptions_segments_whisper_medium_from_db", + "name": "audio_transcriptions_segments_whisper_medium_from_db", "key": "segments", - "path": "video_transcriptions_segments_whisper_medium_from_db", + "path": "audio_transcriptions_segments_whisper_medium_from_db", "data_model": AsrSegments, }, { - "name": "video_transcriptions_whisper_medium_from_db", + "name": "audio_transcriptions_whisper_medium_from_db", "key": "transcription", - "path": "video_transcriptions_whisper_medium_from_db", + "path": "audio_transcriptions_whisper_medium_from_db", "data_model": AsrTranscription, }, ], @@ -784,9 +743,9 @@ "function": "aana.utils.video.generate_combined_timeline", "inputs": [ { - "name": "video_transcriptions_segments_whisper_medium_from_db", + "name": "audio_transcriptions_segments_whisper_medium_from_db", "key": "transcription_segments", - "path": "video_transcriptions_segments_whisper_medium_from_db", + "path": "audio_transcriptions_segments_whisper_medium_from_db", }, { "name": "video_captions_hf_blip2_opt_2_7b_from_db", @@ -936,7 +895,7 @@ ], }, { - "name": "save_video_transcription", + "name": "save_audio_transcription", "type": "function", "function": "aana.utils.db.save_video_transcription", "kwargs": { @@ -950,31 +909,31 @@ "path": "video.media_id", }, { - "name": "video_transcriptions_info_whisper_medium", + "name": "audio_transcriptions_info_whisper_medium", "key": "transcription_info", - "path": "video.transcription_info", + "path": "audio.transcription_info", }, { - "name": "video_transcriptions_segments_whisper_medium", + "name": "audio_transcriptions_segments_whisper_medium", "key": "segments", - "path": "video.segments", + "path": "audio.segments", }, { - "name": "video_transcriptions_whisper_medium", + "name": "audio_transcriptions_whisper_medium", "key": "transcription", - "path": "video.transcription", + "path": "audio.transcription", }, ], "outputs": [ { "name": "transcription_id", "key": "transcription_id", - "path": "video.transcription_id", + "path": "audio.transcription_id", } ], }, { - "name": "save_video_transcription_batched", + "name": "save_audio_transcription_batched", "type": "function", "function": "aana.utils.db.save_video_transcription", "kwargs": { @@ -988,26 +947,26 @@ "path": "video.media_id", }, { - "name": "video_transcriptions_info_batched_whisper_medium", + "name": "audio_transcriptions_info_batched_whisper_medium", "key": "transcription_info", - "path": "video.transcription_info_batched", + "path": "audio.transcription_info_batched", }, { - "name": "video_transcriptions_segments_batched_whisper_medium", + "name": "audio_transcriptions_segments_batched_whisper_medium", "key": "segments", - "path": "video.segments_batched", + "path": "audio.segments_batched", }, { - "name": "video_transcriptions_whisper_medium", + "name": "audio_transcriptions_batched_whisper_medium", "key": "transcription", - "path": "video.transcription_batched", + "path": "audio.transcription_batched", }, ], "outputs": [ { "name": "transcription_id_batched", "key": "transcription_id", - "path": "video.transcription_id_batched", + "path": "audio.transcription_id_batched", } ], }, From b4fe11425140987d923579dcc23900190b841843 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:40:46 +0000 Subject: [PATCH 22/88] added audio_dir --- aana/configs/settings.py | 1 + 1 file changed, 1 insertion(+) diff --git a/aana/configs/settings.py b/aana/configs/settings.py index 0a5634fa..3682e4ce 100644 --- a/aana/configs/settings.py +++ b/aana/configs/settings.py @@ -11,6 +11,7 @@ class Settings(BaseSettings): tmp_data_dir: Path = Path("/tmp/aana_data") # noqa: S108 image_dir = tmp_data_dir / "images" video_dir = tmp_data_dir / "videos" + audio_dir = tmp_data_dir / "audios" db_config: DBConfig = { "datastore_type": "sqlite", From 1d9a7bf97a9df6d0f2296875783ac6a7c6a9422d Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:44:42 +0000 Subject: [PATCH 23/88] input type changes, fixes --- aana/deployments/vad_deployment.py | 103 ++++++++--------------------- 1 file changed, 27 insertions(+), 76 deletions(-) diff --git a/aana/deployments/vad_deployment.py b/aana/deployments/vad_deployment.py index 3c7d6530..4b7bf3cb 100644 --- a/aana/deployments/vad_deployment.py +++ b/aana/deployments/vad_deployment.py @@ -1,34 +1,34 @@ -from pathlib import Path -from contextlib import ExitStack +import hashlib +import subprocess +import urllib.request from collections.abc import Callable -import urllib.request, hashlib -from tqdm import tqdm -from typing import Callable, TypedDict, Optional, Text, Union, Any +from contextlib import ExitStack +from pathlib import Path +from typing import Any, Optional, TypedDict -import torch -import subprocess import numpy as np - -# faster whisper here? or load audio? +import torch from pyannote.audio import Model from pyannote.audio.core.io import AudioFile from pyannote.audio.pipelines import VoiceActivityDetection from pyannote.audio.pipelines.utils import PipelineModel from pyannote.core import Annotation, Segment, SlidingWindowFeature +from pydantic import BaseModel, Field +from ray import serve +from tqdm import tqdm -from aana.models.core.video import Video from aana.deployments.base_deployment import BaseDeployment from aana.exceptions.general import InferenceException -from aana.models.pydantic.vad_params import VadParams -from aana.models.pydantic.vad_output import VadSegment +from aana.models.core.audio import Audio from aana.models.pydantic.time_interval import TimeInterval - -from ray import serve -from pydantic import BaseModel, Field +from aana.models.pydantic.vad_output import VadSegment +from aana.models.pydantic.vad_params import VadParams class SegmentX: - def __init__(self, start, end, speaker=None): + """The vad segment format with optional speaker.""" + + def __init__(self, start, end, speaker=None): # noqa: D107 self.start = start self.end = end self.speaker = speaker @@ -45,7 +45,7 @@ class VadOutput(TypedDict): class VadConfig(BaseModel): - """The configuration for the whisper deployment from faster-whisper.""" + """The configuration for the vad deployment.""" model_fp: Path | None = Field(default=None, description="The VAD model path.") @@ -62,11 +62,11 @@ class VoiceActivitySegmentation(VoiceActivityDetection): segmentations: segmented speech regions """ - def __init__( + def __init__( # noqa: D107 self, segmentation: PipelineModel = "pyannote/segmentation", fscore: bool = False, - use_auth_token: Union[Text, None] = None, + use_auth_token: str | None = None, **inference_kwargs, ): super().__init__( @@ -76,7 +76,7 @@ def __init__( **inference_kwargs, ) - def apply(self, file: AudioFile, hook: Optional[Callable] = None) -> Annotation: + def apply(self, file: AudioFile, hook: Callable | None = None) -> Annotation: """Apply voice activity detection. Args: @@ -318,46 +318,6 @@ def download_vad_model(model_fp, vad_segmentation_url): ) self.vad_pipeline.instantiate(hyperparameters) - # OPTIONAL: audio loader function if asr model is not used in deployment. - async def load_audio(self, file: str, sr: int = 16000): - """Open an audio file and read as mono waveform, resampling as necessary. - - Args: - file (str): The audio file to open. - sr (int): The sample rate to resample the audio if necessary. - - Returns: - A NumPy array containing the audio waveform, in float32 dtype. - - Raises: - RuntimeError: if ffmpeg fails to convert and load the audio. - """ - try: - # Launches a subprocess to decode audio while down-mixing and resampling as necessary. - # Requires the ffmpeg CLI to be installed. - cmd = [ - "ffmpeg", - "-nostdin", - "-threads", - "0", - "-i", - file, - "-f", - "s16le", - "-ac", - "1", - "-acodec", - "pcm_s16le", - "-ar", - str(sr), - "-", - ] - out = subprocess.run(cmd, capture_output=True, check=True).stdout - except subprocess.CalledProcessError as e: - raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e - - return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 - async def merge_chunks( self, segments: list[dict], params: dict[str, Any] ) -> list[VadSegment]: @@ -398,7 +358,7 @@ async def merge_chunks( # No active speech found in audio. return [] - # Make sure the starting point is the start of the segment. + # To make sure the starting point is the start of the segment. curr_start = segments_list[0].start for seg in segments_list: @@ -424,11 +384,11 @@ async def merge_chunks( ) return merged_segments - async def vad_inference(self, media: Video) -> list[dict]: - """Perform voice activity detection on the media with the vad model. + async def vad_inference(self, media: Audio) -> list[dict]: + """Perform voice activity detection on the Audio with the vad model. Args: - media (Video): The media to perform vad. + media (Audio): The audio to perform vad. Returns: vad_segments (list[dict]): The list of vad segments. @@ -436,17 +396,8 @@ async def vad_inference(self, media: Video) -> list[dict]: Raises: InferenceException: If the vad inference fails. """ - media_path: str = str(media.path) - # load audio data - audio_array = ( - np.frombuffer(open(media_path, "rb").read(), dtype=np.int16) - .flatten() - .astype(np.float32) - / 32768.0 - ) - # OPTIONAL: Use load_audio function if audio extraction is not performed. - # audio_array = self.load_audio(media_path) + audio_array = media.get_numpy() try: vad_segments = self.vad_pipeline( @@ -461,12 +412,12 @@ async def vad_inference(self, media: Video) -> list[dict]: return vad_segments async def asr_preprocess_vad( - self, media: Video, params: VadParams | None = None + self, media: Audio, params: VadParams | None = None ) -> VadOutput: """Perform vad inference and further processing for batched asr inference. Args: - media (Video): The media to perform vad. + media (Audio): The audio to perform vad. params (VadParams): The parameters for the vad model. Returns: From 5e67eff166cc9d6b027ac96b1da9ed2780defb5b Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:45:00 +0000 Subject: [PATCH 24/88] input type changes, fixes --- aana/deployments/whisper_deployment.py | 77 +++++++++----------------- 1 file changed, 26 insertions(+), 51 deletions(-) diff --git a/aana/deployments/whisper_deployment.py b/aana/deployments/whisper_deployment.py index 6e9f0784..b65637fd 100644 --- a/aana/deployments/whisper_deployment.py +++ b/aana/deployments/whisper_deployment.py @@ -1,6 +1,5 @@ from collections.abc import AsyncGenerator from enum import Enum -import numpy as np from types import SimpleNamespace from typing import Any, TypedDict, cast @@ -8,7 +7,6 @@ from faster_whisper import WhisperModel, BatchedInferencePipeline from faster_whisper.tokenizer import Tokenizer from faster_whisper.transcribe import TranscriptionOptions -from faster_whisper.audio import decode_audio from pydantic import BaseModel, Field @@ -16,7 +14,8 @@ from aana.deployments.base_deployment import BaseDeployment from aana.exceptions.general import InferenceException -from aana.models.core.video import Video +from aana.models.core.audio import Audio + from aana.models.pydantic.asr_output import ( AsrSegment, AsrTranscription, @@ -135,9 +134,7 @@ async def apply_config(self, config: dict[str, Any]): The method is called when the deployment is created or updated. - It loads the model and processor from HuggingFace. - - The configuration should conform to the HFBlip2Config schema. + The configuration should conform to the WhisperConfig schema. """ config_obj = WhisperConfig(**config) self.model_size = config_obj.model_size @@ -148,14 +145,13 @@ async def apply_config(self, config: dict[str, Any]): self.model_size, device=self.device, compute_type=self.compute_type ) - # TODO: add audio support async def transcribe( - self, media: Video, params: WhisperParams | None = None + self, media: Audio, params: WhisperParams | None = None ) -> WhisperOutput: - """Transcribe the media with the whisper model. + """Transcribe the audio with the whisper model. Args: - media (Video): The media to transcribe. + media (Audio): The audio to transcribe. params (WhisperParams): The parameters for the whisper model. Returns: @@ -169,14 +165,9 @@ async def transcribe( """ if not params: params = WhisperParams() - media_path: str = str(media.path) - # load from buffer and pass it - audio_array = ( - np.frombuffer(open(media_path, "rb").read(), dtype=np.int16) - .flatten() - .astype(np.float32) - / 32768.0 - ) + + audio_array = media.get_numpy() + try: segments, info = self.model.transcribe(audio_array, **params.dict()) except Exception as e: @@ -194,12 +185,12 @@ async def transcribe( ) async def transcribe_stream( - self, media: Video, params: WhisperParams | None = None + self, media: Audio, params: WhisperParams | None = None ) -> AsyncGenerator[WhisperOutput, None]: - """Transcribe the media with the whisper model in a streaming fashion. + """Transcribe the audio with the whisper model in a streaming fashion. Args: - media (Video): The media to transcribe. + media (Audio): The audio to transcribe. params (WhisperParams): The parameters for the whisper model. Yields: @@ -210,9 +201,9 @@ async def transcribe_stream( """ if not params: params = WhisperParams() - media_path: str = str(media.path) + audio_array = media.get_numpy() try: - segments, info = self.model.transcribe(media_path, **params.dict()) + segments, info = self.model.transcribe(audio_array, **params.dict()) except Exception as e: raise InferenceException(self.model_name) from e @@ -228,19 +219,19 @@ async def transcribe_stream( ) async def transcribe_batch( - self, media_batch: list[Video], params: WhisperParams = None + self, media_batch: list[Audio], params: WhisperParams = None ) -> WhisperBatchOutput: - """Transcribe the batch of media with the Whisper model. + """Transcribe the batch of audios with the Whisper model. Args: - media_batch (list[Video]): The batch of media to transcribe. + media_batch (list[Audio]): The batch of audios to transcribe. params (WhisperParams): The parameters for the whisper model. Returns: WhisperBatchOutput: The transcription output as a dictionary: - segments (list[list[AsrSegment]]): The ASR segments for each media. - transcription_info (list[AsrTranscriptionInfo]): The ASR transcription info for each media. - transcription (list[AsrTranscription]): The ASR transcription for each media. + segments (list[list[AsrSegment]]): The ASR segments for each audio. + transcription_info (list[AsrTranscriptionInfo]): The ASR transcription info for each audio. + transcription (list[AsrTranscription]): The ASR transcription for each audio. Raises: InferenceException: If the inference fails. @@ -262,15 +253,15 @@ async def transcribe_batch( async def batched_inference( self, - media: Video, + media: Audio, vad_segments: list[VadSegment], batch_size: int = 16, params: WhisperParams | None = None, ) -> AsyncGenerator[WhisperOutput, None]: - """Transcribe the batch of audio segments with the Whisper model. + """Transcribe a single audio as batches of segments with the Whisper model (4x faster). Args: - media (Video): The media to transcribe. / audio_data (np.ndarray): The audio numpy array to transcribe. + media (Audio): The media to transcribe. vad_segments (list[VadSegment]): List of segments to guide batching the audio data. batch_size (int): Maximum batch size for the batched inference. params (WhisperParams): The parameters for the whisper model. @@ -288,7 +279,6 @@ async def batched_inference( if not params: params = WhisperParams() - # additional param: batch size for batched asr if params.language is not None: tokenizer = Tokenizer( self.model.hf_tokenizer, @@ -297,9 +287,7 @@ async def batched_inference( language=params.language, ) else: - self.model.logger.warning( - "No language specified, language will be first detected for each audio file." - ) + # If no language is specified, language will be first detected for each audio. tokenizer = None default_asr_options = TranscriptionOptions(**default_batched_asr_options) @@ -311,23 +299,12 @@ async def batched_inference( language=params.language, ) - media_path: str = str(media.path) - - # OPTIONAL: Use faster_whisper function to load media or use from buffer for loading extracted audio - # audio_data = decode_audio(media_path) - # TODO: raise value error if not able to load the audio - - audio_array = ( - np.frombuffer(open(media_path, "rb").read(), dtype=np.int16) - .flatten() - .astype(np.float32) - / 32768.0 - ) + audio_array = media.get_numpy() vad_input = [seg.to_dict() for seg in vad_segments] if not vad_input: - # return empty output with language as silence for silent audios + # For silent audios/no audio tracks, return empty output with language as silence info = {"language": "silence", "language_probability": 1.0} yield WhisperOutput( segments=[], @@ -346,7 +323,6 @@ async def batched_inference( except Exception as e: raise InferenceException(self.model_name) from e - # count = 0 for count, (segment, info) in enumerate(result): if count == 0: # converting info dictionary to an object: @@ -354,7 +330,6 @@ async def batched_inference( asr_transcription_info = AsrTranscriptionInfo.from_whisper( transcription_object ) - # count += 1 asr_segments = [AsrSegment.from_whisper(segment)] asr_transcription = AsrTranscription(text=segment.text) From a25c8ced08cc693047421a7894d94f8199d8d3bf Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:45:28 +0000 Subject: [PATCH 25/88] added AudioReadingException --- aana/exceptions/general.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/aana/exceptions/general.py b/aana/exceptions/general.py index 23a3e581..75b9a87a 100644 --- a/aana/exceptions/general.py +++ b/aana/exceptions/general.py @@ -5,6 +5,7 @@ if TYPE_CHECKING: from aana.models.core.image import Image from aana.models.core.video import Video + from aana.models.core.audio import Audio class InferenceException(BaseException): @@ -75,6 +76,27 @@ def __reduce__(self): return (self.__class__, (self.image,)) +class AudioReadingException(BaseException): + """Exception raised when there is an error reading an audio. + + Attributes: + audio (Audio): the audio that caused the exception + """ + + def __init__(self, audio: "Audio"): + """Initialize the exception. + + Args: + audio (Audio): the audio that caused the exception + """ + super().__init__(image=audio) + self.audio = audio + + def __reduce__(self): + """Used for pickling.""" + return (self.__class__, (self.audio,)) + + class DownloadException(BaseException): """Exception raised when there is an error downloading a file. From c0a641d3eb175833ee01623d92dd5b864c5f3da6 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:45:47 +0000 Subject: [PATCH 26/88] added core Audio dataclass --- aana/models/core/audio.py | 294 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 294 insertions(+) create mode 100644 aana/models/core/audio.py diff --git a/aana/models/core/audio.py b/aana/models/core/audio.py new file mode 100644 index 00000000..831e6b5e --- /dev/null +++ b/aana/models/core/audio.py @@ -0,0 +1,294 @@ +import hashlib # noqa: I001 +from dataclasses import dataclass +from pathlib import Path +import torch, decord # noqa: F401 # See https://github.com/dmlc/decord/issues/263 +from decord import DECORDError +import numpy as np +import wave +from aana.configs.settings import settings +from aana.exceptions.general import AudioReadingException +from aana.models.core.media import Media +from aana.utils.general import download_file + + +class AbstractAudioLibrary: + """Abstract class for audio libraries.""" + + @classmethod + def read_file(cls, path: Path) -> np.ndarray: + """Read an audio file from path. + + Args: + path (Path): The path of the file to read. + + Returns: + np.ndarray: The audio file as a numpy array. + """ + audio = ( + np.frombuffer(open(str(path), "rb").read(), dtype=np.int16) + .flatten() + .astype(np.float32) + / 32768.0 + ) + return audio + + @classmethod + def read_from_bytes(cls, content: bytes) -> np.ndarray: + """Read audio bytes as numpy array. + + Args: + content (bytes): The content of the file to read. + + Returns: + np.ndarray: The file as a numpy array. + """ + audio = ( + np.frombuffer(content, dtype=np.int16).flatten().astype(np.float32) + / 32768.0 + ) + return audio + + @classmethod + def write_file(cls, path: Path, audio: np.ndarray): + """Write a file to wav from numpy array. + + Args: + path (Path): The path of the file to write. + audio (np.ndarray): The audio to write. + """ + normalized_audio = np.int16(audio * 32767) + + # Open a WAV file for writing + with wave.open(str(path), "w") as wav_file: + # Set the WAV file parameters + wav_file.setnchannels(1) # Mono audio + wav_file.setsampwidth(2) # 2 bytes (16 bits) per sample + wav_file.setframerate(16000) # Sample rate + wav_file.writeframes(normalized_audio.tobytes()) + + @classmethod + def write_audio_bytes(cls, path: Path, audio: bytes): + """Write a file to wav from the normalized audio bytes. + + Args: + path (Path): The path of the file to write. + audio (bytes): The audio to in 16-bit PCM byte write. + """ + with wave.open(str(path), "wb") as wav_file: + wav_file.setnchannels(1) # Mono audio + wav_file.setsampwidth(2) # 16-bit audio + wav_file.setframerate(16000) # Sample rate + wav_file.writeframes(audio) + + +@dataclass +class Audio(Media): + """A class representing an audio. + + At least one of 'path', 'url', or 'content' must be provided. + If 'save_on_disk' is True, the audio will be saved on disk automatically. + + Attributes: + path (Path): the path to the audio file + url (str): the URL of the audio + content (bytes): the content of the audio in bytes + media_id (str): the ID of the audio. If not provided, it will be generated automatically. + title (str): the title of the audio + description (str): the description of the audio + audio_dir (Path): the directory to save the audio in + """ + + title: str = "" + description: str = "" + audio_dir: Path | None = settings.audio_dir + numpy: np.ndarray | None = None + audio_lib: type[AbstractAudioLibrary] = AbstractAudioLibrary + + def validate(self): + """Validate the audio. + + Raises: + ValueError: if none of 'path', 'url', or 'content' is provided + AudioReadingException: if the audio is not valid + """ + # validate the parent class + super().validate() + + # check that at least one of 'path', 'url' or 'content' is provided + if not any( + [ + self.path is not None, + self.url is not None, + self.content is not None, + ] + ): + raise ValueError( # noqa: TRY003 + "At least one of 'path', 'url' or 'content' must be provided." + ) + + # check that the audio is valid + if self.path and not self.is_audio(): + raise AudioReadingException(video=self) + + def is_audio(self) -> bool: + """Checks if it's a valid audio.""" + if not self.path: + return False + try: + decord.AudioReader(str(self.path)) + except DECORDError: + return False + return True + + def save(self): + """Save the image on disk. + + If the audio is already available on disk, do nothing. + If the audio represented as a byte string, save it on disk. + If the audio is represented as a URL, download it and save it on disk. + If the audio is represented as a numpy array, convert it to audio wav and save it on disk. + + First check if the audio is already available on disk, then content, then url, then numpy + to avoid unnecessary operations (e.g. downloading the audio or converting it to wav). + + Raises: + ValueError: If none of 'path', 'url', 'content' or 'numpy' is provided. + """ + if self.path: + return + + audio_dir = settings.audio_dir + audio_dir.mkdir(parents=True, exist_ok=True) + file_path = audio_dir / (self.media_id + ".wav") + if self.content is not None: + self.save_from_audio_content(file_path) + elif self.numpy is not None: + self.save_from_numpy(file_path) + elif self.url: + self.save_from_audio_url(file_path) + else: + raise ValueError( # noqa: TRY003 + "At least one of 'path', 'url', 'content' or 'numpy' must be provided." + ) + self.path = file_path + self.is_saved = True + + def save_from_audio_content(self, file_path: Path): + """Save the audio from the content. + + Args: + file_path (Path): the path to save the audio to + """ + assert self.content is not None # noqa: S101 + self.audio_lib.write_audio_bytes(file_path, self.content) + + def save_from_audio_url(self, file_path): + """Save the audio from the URL. + + Args: + file_path (Path): the path to save the audio to + + Raises: + DownloadError: if the audio can't be downloaded + """ + assert self.url is not None # noqa: S101 + content: bytes = download_file(self.url) + self.audio_lib.write_audio_bytes(file_path, content) + + def save_from_numpy(self, file_path: Path): + """Save the audio from numpy on disk. + + Args: + file_path (Path): The path of the file to write. + """ + assert self.numpy is not None # noqa: S101 TODO + self.audio_lib.write_file(file_path, self.numpy) + + def get_numpy(self) -> np.ndarray: + """Load the audio as a numpy array. + + Returns: + np.ndarray: The audio as a numpy array. + + Raises: + ValueError: If none of 'path', 'url', 'content' or 'numpy' is provided. + AudioReadingException: If there is an error reading the audio. + """ + if self.numpy is not None: + return self.numpy + elif self.path: + self.load_numpy_from_path() + elif self.url: + self.load_numpy_from_url() + elif self.content: + self.load_numpy_from_content() + else: + raise ValueError( # noqa: TRY003 + "At least one of 'path', 'url', 'content' or 'numpy' must be provided." + ) + assert self.numpy is not None # noqa: S101 + return self.numpy + + def load_numpy_from_path(self): + """Load the audio as a numpy array from a path. + + Raises: + ImageReadingException: If there is an error reading the audio. + """ + assert self.path is not None # noqa: S101 + try: + self.numpy = self.audio_lib.read_file(self.path) + except Exception as e: + raise AudioReadingException(self) from e + + def load_numpy_from_audio_bytes(self, audio_bytes: bytes): + """Load the image as a numpy array from image bytes (downloaded from URL or read from file). + + Raises: + ImageReadingException: If there is an error reading the image. + """ + try: + self.numpy = self.audio_lib.read_from_bytes(audio_bytes) + except Exception as e: + raise AudioReadingException(self) from e + + def load_numpy_from_url(self): + """Load the audio as a numpy array from a URL. + + Raises: + AudioReadingException: If there is an error reading the audio. + """ + assert self.url is not None # noqa: S101 + content: bytes = download_file(self.url) + self.load_numpy_from_audio_bytes(content) + + def load_numpy_from_content(self): + """Load the image as a numpy array from content. + + Raises: + ImageReadingException: If there is an error reading the image. + """ + assert self.content is not None # noqa: S101 + self.load_numpy_from_audio_bytes(self.content) + + def __repr__(self) -> str: + """Get the representation of the audio. + + Use md5 hash for the content of the audio if it is available. + + Returns: + str: the representation of the audio + """ + content_hash = ( + hashlib.md5(self.content, usedforsecurity=False).hexdigest() + if self.content + else None + ) + return ( + f"Audio(path={self.path}, " + f"url={self.url}, " + f"content={content_hash}, " + f"media_id={self.media_id}, " + f"title={self.title}, " + f"description={self.description})" + ) From 399a8f2ca69032492a3174a165cca4d09e055386 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:46:06 +0000 Subject: [PATCH 27/88] tests for vad deployment --- aana/tests/deployments/test_vad_deployment.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) diff --git a/aana/tests/deployments/test_vad_deployment.py b/aana/tests/deployments/test_vad_deployment.py index e69de29b..a27a3be5 100644 --- a/aana/tests/deployments/test_vad_deployment.py +++ b/aana/tests/deployments/test_vad_deployment.py @@ -0,0 +1,86 @@ +import json +from importlib import resources +from pathlib import Path + +import pytest +import ray +from deepdiff import DeepDiff +from ray import serve + +from aana.configs.deployments import deployments +from aana.models.core.audio import Audio +from aana.models.pydantic.vad_params import VadParams +from aana.tests.utils import LevenshteinOperator, is_gpu_available +from aana.utils.general import pydantic_to_dict + +EPSILON = 0.01 + + +def compare_vad_outputs(expected_output, predictions): + """Compare two vad outputs. + + Start, end and number of segments inside are compared using Levenshtein distance. + + Args: + expected_output (list[dict]): the expected vad_output + predictions (list[dict]): the predictions of vad + + Raises: + AssertionError: if vad_outputs differ too much + """ + for expected_output_seg, predictions_seg in zip( + expected_output, predictions, strict=False + ): + diff = DeepDiff( + expected_output_seg, + predictions_seg, + math_epsilon=EPSILON, + ignore_numeric_type_changes=True, + custom_operators=[LevenshteinOperator([r"\['start'\]$", r"\['end'\]$"])], + ) + print(diff) + assert not diff, diff # noqa: S101 + + +def ray_setup(deployment): + """Setup Ray instance for the test.""" + # Setup ray environment and serve + ray.init(ignore_reinit_error=True) + app = deployment.bind() + port = 34422 + test_name = deployment.name + route_prefix = f"/{test_name}" + handle = serve.run(app, port=port, name=test_name, route_prefix=route_prefix) + return handle + + +@pytest.mark.skipif(not is_gpu_available(), reason="GPU is not available") +@pytest.mark.asyncio +@pytest.mark.parametrize("video_file", ["physicsworks_16k.wav"]) # need this. +async def test_whisper_deployment(video_file): + """Test whisper deployment.""" + for deployment in deployments.values(): + # skip if not vad deployment + if deployment.name != "VadDeployment": + continue + + handle = ray_setup(deployment) + + expected_output_path = resources.path( + "aana.tests.files.expected.vad", f"{video_file}_vad.json" + ) + assert ( # noqa: S101 + expected_output_path.exists() + ), f"Expected output not found: {expected_output_path}" + with Path(expected_output_path) as path, path.open() as f: + expected_output = json.load(f) + + # asr_preprocess_vad method + path = resources.path("aana.tests.files.audios", video_file) + assert path.exists(), f"Audio not found: {path}" + audio = Audio(path=path) + + output = await handle.asr_preprocess_vad.remote(media=audio, params=VadParams) + output = pydantic_to_dict(output) + + compare_vad_outputs(expected_output, output) From 2961ab0d21d33a0f8ced026ad3f59ce8a51ff5c3 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:46:25 +0000 Subject: [PATCH 28/88] updated tests for whisper deployment --- .../deployments/test_whisper_deployment.py | 70 ++++++++++++++----- 1 file changed, 51 insertions(+), 19 deletions(-) diff --git a/aana/tests/deployments/test_whisper_deployment.py b/aana/tests/deployments/test_whisper_deployment.py index b79cd920..fbf0347e 100644 --- a/aana/tests/deployments/test_whisper_deployment.py +++ b/aana/tests/deployments/test_whisper_deployment.py @@ -10,7 +10,7 @@ from ray import serve from aana.configs.deployments import deployments -from aana.models.core.video import Video +from aana.models.core.audio import Audio from aana.models.pydantic.whisper_params import WhisperParams from aana.tests.utils import LevenshteinOperator, is_gpu_available from aana.utils.general import pydantic_to_dict @@ -18,7 +18,7 @@ EPSILON = 0.01 -def compare_transcriptions(expected_transcription, transcription): +def compare_transcriptions(expected_transcription, transcription, words=True): """Compare two transcriptions. Texts and words are compared using Levenshtein distance. @@ -30,12 +30,18 @@ def compare_transcriptions(expected_transcription, transcription): Raises: AssertionError: if transcriptions differ too much """ + + if words: + levenshtein_operator = [LevenshteinOperator([r"\['text'\]$", r"\['word'\]$"])] + else: + levenshtein_operator = [LevenshteinOperator([r"\['text'\]$"])] + diff = DeepDiff( expected_transcription, transcription, math_epsilon=EPSILON, ignore_numeric_type_changes=True, - custom_operators=[LevenshteinOperator([r"\['text'\]$", r"\['word'\]$"])], + custom_operators= levenshtein_operator, ) assert not diff, diff @@ -54,11 +60,11 @@ def ray_setup(deployment): @pytest.mark.skipif(not is_gpu_available(), reason="GPU is not available") @pytest.mark.asyncio -@pytest.mark.parametrize("video_file", ["physicsworks.webm"]) -async def test_whisper_deployment(video_file): +@pytest.mark.parametrize("audio_file", ["physicsworks_16k.wav"]) +async def test_whisper_deployment(audio_file): """Test whisper deployment.""" for deployment in deployments.values(): - # skip if not a VLLM deployment + # skip if not a whisper deployment if deployment.name != "WhisperDeployment": continue @@ -67,7 +73,7 @@ async def test_whisper_deployment(video_file): model_size = deployment.user_config["model_size"] expected_output_path = resources.path( - f"aana.tests.files.expected.whisper.{model_size}", f"{video_file}.json" + f"aana.tests.files.expected.whisper.{model_size}", f"{audio_file}.json" ) assert ( expected_output_path.exists() @@ -76,24 +82,20 @@ async def test_whisper_deployment(video_file): expected_output = json.load(f) # Test transcribe method - path = resources.path("aana.tests.files.videos", video_file) - assert path.exists(), f"Video not found: {path}" - video = Video(path=path) + path = resources.path("aana.tests.files.audios", audio_file) + assert path.exists(), f"Audio not found: {path}" + audio = Audio(path=path) output = await handle.transcribe.remote( - media=video, params=WhisperParams(word_timestamps=True) + media=audio, params=WhisperParams(word_timestamps=True) ) output = pydantic_to_dict(output) compare_transcriptions(expected_output, output) # Test transcribe_stream method - path = resources.path("aana.tests.files.videos", video_file) - assert path.exists(), f"Video not found: {path}" - video = Video(path=path) - stream = handle.options(stream=True).transcribe_stream.remote( - media=video, params=WhisperParams(word_timestamps=True) + media=audio, params=WhisperParams(word_timestamps=True) ) # Combine individual segments and compare with the final dict @@ -110,13 +112,43 @@ async def test_whisper_deployment(video_file): compare_transcriptions(expected_output, dict(grouped_dict)) # Test transcribe_batch method - videos = [video, video] + audios = [audio, audio] batch_output = await handle.transcribe_batch.remote( - media_batch=videos, params=WhisperParams(word_timestamps=True) + media_batch=audios, params=WhisperParams(word_timestamps=True) ) batch_output = pydantic_to_dict(batch_output) - for i in range(len(videos)): + for i in range(len(audios)): output = {k: v[i] for k, v in batch_output.items()} compare_transcriptions(expected_output, output) + + # Test batched_inference method: expected asr output is different + expected_batched_output_path = + # get expected vad_segments + vad_path = resources.path( + "aana.tests.files.expected.vad", f"{audio_file}_vad.json" + ) + assert vad_path.exists(), f"vad expected predictions not found: {vad_path}" + + with Path(vad_path) as path, path.open() as f: + expected_output_vad = json.load(f) + + # load expected_transcription_batched for batched version. check info + + stream = handle.options(stream=True).batched_inference.remote( + media=audio, # chnge alls + vad_segments=expected_output_vad, + batch_size=16, + params=WhisperParams, + ) + + # Combine individual segments and compare with the final dict + grouped_dict = defaultdict(list) + async for chunk in stream: + chunk = await chunk + output = pydantic_to_dict(chunk) + grouped_dict["segments"].append(output.get("segments")[0]["text"]) + + grouped_dict["transcription_info"] = output.get("transcription_info") + compare_transcriptions(expected_transcription_batched, dict(grouped_dict), words=False) From ef503ecea92011a751b2f22f8f85e59e811fa968 Mon Sep 17 00:00:00 2001 From: jiltseb Date: Fri, 9 Feb 2024 13:49:30 +0000 Subject: [PATCH 29/88] adding test audio file --- aana/tests/files/audios/physicsworks_16k.wav | Bin 0 -> 6505148 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 aana/tests/files/audios/physicsworks_16k.wav diff --git a/aana/tests/files/audios/physicsworks_16k.wav b/aana/tests/files/audios/physicsworks_16k.wav new file mode 100644 index 0000000000000000000000000000000000000000..885b6c1c88f3a0baea7738a7d8f6fc68ef277b49 GIT binary patch literal 6505148 zcmX_o1(*~^*LAhq_&V+o+}(n^2PY69Bte2paCaxTLvVL@C-~yB%ewFQw0Hk^Ht+Xu 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