[Tests] Correct Wav2Vec2 & WavLM tests (#15015)

* up

* up

* up

.github/workflows/self-scheduled.yml

@@ -290,7 +290,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          apt -y update && apt install -y libsndfile1-dev git
+          apt -y update && apt install -y libsndfile1-dev git espeak-ng
           pip install --upgrade pip
           pip install .[sklearn,testing,onnx,sentencepiece,tf-speech,vision]
           pip install https://github.com/kpu/kenlm/archive/master.zip

tests/test_modeling_tf_wav2vec2.py

@@ -15,6 +15,7 @@
 
 
 import copy
+import glob
 import inspect
 import math
 import unittest
@@ -23,6 +24,7 @@
 import pytest
 from datasets import load_dataset
 
+from huggingface_hub import snapshot_download
 from transformers import Wav2Vec2Config, is_tf_available
 from transformers.file_utils import is_librosa_available, is_pyctcdecode_available
 from transformers.testing_utils import require_librosa, require_pyctcdecode, require_tf, slow
@@ -485,8 +487,6 @@ def test_compute_mask_indices_overlap(self):
 @slow
 class TFWav2Vec2ModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):
-        from datasets import load_dataset
-
         ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").filter(
@@ -556,18 +556,17 @@ def test_inference_ctc_robust_batched(self):
     @require_pyctcdecode
     @require_librosa
     def test_wav2vec2_with_lm(self):
-        ds = load_dataset("common_voice", "es", split="test", streaming=True)
-        sample = next(iter(ds))
-
-        resampled_audio = librosa.resample(sample["audio"]["array"], 48_000, 16_000)
+        downloaded_folder = snapshot_download("patrickvonplaten/common_voice_es_sample")
+        file_path = glob.glob(downloaded_folder + "/*")[0]
+        sample = librosa.load(file_path, sr=16_000)[0]
 
         model = TFWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
         processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
 
-        input_values = processor(resampled_audio, return_tensors="tf").input_values
+        input_values = processor(sample, return_tensors="tf").input_values
 
         logits = model(input_values).logits
 
         transcription = processor.batch_decode(logits.numpy()).text
 
-        self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
+        self.assertEqual(transcription[0], "el libro ha sido escrito por cervantes")

tests/test_modeling_wavlm.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """ Testing suite for the PyTorch WavLM model. """
 
-import copy
 import math
 import unittest
 
@@ -452,30 +451,9 @@ def _mock_init_weights(self, module):
         if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
             module.masked_spec_embed.data.fill_(3)
 
-    # overwrite from test_modeling_common
-    # as WavLM is not very precise
+    @unittest.skip(reason="Feed forward chunking is not implemented for WavLM")
     def test_feed_forward_chunking(self):
-        (
-            original_config,
-            inputs_dict,
-        ) = self.model_tester.prepare_config_and_inputs_for_common()
-        for model_class in self.all_model_classes:
-            torch.manual_seed(0)
-            config = copy.deepcopy(original_config)
-            model = model_class(config)
-            model.to(torch_device)
-            model.eval()
-
-            hidden_states_no_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
-
-            torch.manual_seed(0)
-            config.chunk_size_feed_forward = 1
-            model = model_class(config)
-            model.to(torch_device)
-            model.eval()
-
-            hidden_states_with_chunk = model(**self._prepare_for_class(inputs_dict, model_class))[0]
-            self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-2))
+        pass
 
     @slow
     def test_model_from_pretrained(self):
@@ -528,7 +506,7 @@ def test_inference_base(self):
     def test_inference_large(self):
         model = WavLMModel.from_pretrained("microsoft/wavlm-large").to(torch_device)
         feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
-            "microsoft/wavlm-base-plus", return_attention_mask=True
+            "microsoft/wavlm-large", return_attention_mask=True
         )
 
         input_speech = self._load_datasamples(2)
@@ -544,8 +522,9 @@ def test_inference_large(self):
             )
 
         EXPECTED_HIDDEN_STATES_SLICE = torch.tensor(
-            [[[0.1612, 0.4314], [0.1690, 0.4344]], [[0.2086, 0.1396], [0.3014, 0.0903]]]
+            [[[0.2122, 0.0500], [0.2118, 0.0563]], [[0.1353, 0.1818], [0.2453, 0.0595]]]
         )
+
         self.assertTrue(torch.allclose(hidden_states_slice, EXPECTED_HIDDEN_STATES_SLICE, rtol=5e-2))
 
     def test_inference_diarization(self):