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Fix some tests using "common_voice" (#27147)
* Use mozilla-foundation/common_voice_11_0 * Update expected values * Update expected values * For test_word_time_stamp_integration --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar
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@ -97,7 +97,7 @@ def _test_wav2vec2_with_lm_invalid_pool(in_queue, out_queue, timeout):
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try:
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_ = in_queue.get(timeout=timeout)
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ds = load_dataset("common_voice", "es", split="test", streaming=True)
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ds = load_dataset("mozilla-foundation/common_voice_11_0", "es", split="test", streaming=True)
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sample = next(iter(ds))
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resampled_audio = torchaudio.functional.resample(
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@ -119,7 +119,7 @@ def _test_wav2vec2_with_lm_invalid_pool(in_queue, out_queue, timeout):
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transcription = processor.batch_decode(logits.cpu().numpy(), pool).text
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unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
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unittest.TestCase().assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
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unittest.TestCase().assertEqual(transcription[0], "habitan aguas poco profundas y rocosas")
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# force batch_decode to internally create a spawn pool, which should trigger a warning if different than fork
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multiprocessing.set_start_method("spawn", force=True)
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@ -127,7 +127,7 @@ def _test_wav2vec2_with_lm_invalid_pool(in_queue, out_queue, timeout):
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transcription = processor.batch_decode(logits.cpu().numpy()).text
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unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
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unittest.TestCase().assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
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unittest.TestCase().assertEqual(transcription[0], "habitan aguas poco profundas y rocosas")
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except Exception:
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error = f"{traceback.format_exc()}"
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@ -1833,7 +1833,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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@require_pyctcdecode
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@require_torchaudio
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def test_wav2vec2_with_lm(self):
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ds = load_dataset("common_voice", "es", split="test", streaming=True)
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ds = load_dataset("mozilla-foundation/common_voice_11_0", "es", split="test", streaming=True)
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sample = next(iter(ds))
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resampled_audio = torchaudio.functional.resample(
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@ -1852,12 +1852,12 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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transcription = processor.batch_decode(logits.cpu().numpy()).text
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self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
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self.assertEqual(transcription[0], "habitan aguas poco profundas y rocosas")
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@require_pyctcdecode
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@require_torchaudio
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def test_wav2vec2_with_lm_pool(self):
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ds = load_dataset("common_voice", "es", split="test", streaming=True)
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ds = load_dataset("mozilla-foundation/common_voice_11_0", "es", split="test", streaming=True)
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sample = next(iter(ds))
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resampled_audio = torchaudio.functional.resample(
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@ -1878,7 +1878,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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with multiprocessing.get_context("fork").Pool(2) as pool:
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transcription = processor.batch_decode(logits.cpu().numpy(), pool).text
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self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
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self.assertEqual(transcription[0], "habitan aguas poco profundas y rocosas")
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# user-managed pool + num_processes should trigger a warning
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with CaptureLogger(processing_wav2vec2_with_lm.logger) as cl, multiprocessing.get_context("fork").Pool(
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@ -1889,7 +1889,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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self.assertIn("num_process", cl.out)
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self.assertIn("it will be ignored", cl.out)
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self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
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self.assertEqual(transcription[0], "habitan aguas poco profundas y rocosas")
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@require_pyctcdecode
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@require_torchaudio
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@ -1957,7 +1957,7 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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LANG_MAP = {"it": "ita", "es": "spa", "fr": "fra", "en": "eng"}
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def run_model(lang):
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ds = load_dataset("common_voice", lang, split="test", streaming=True)
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ds = load_dataset("mozilla-foundation/common_voice_11_0", lang, split="test", streaming=True)
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sample = next(iter(ds))
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wav2vec2_lang = LANG_MAP[lang]
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@ -1982,10 +1982,10 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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return transcription
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TRANSCRIPTIONS = {
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"it": "mi hanno fatto un'offerta che non potevo proprio rifiutare",
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"es": "bien y qué regalo vas a abrir primero",
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"fr": "un vrai travail intéressant va enfin être mené sur ce sujet",
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"en": "twas the time of day and olof spen slept during the summer",
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"it": "il libro ha suscitato molte polemiche a causa dei suoi contenuti",
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"es": "habitan aguas poco profundas y rocosas",
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"fr": "ce dernier est volé tout au long de l'histoire romaine",
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"en": "joe keton disapproved of films and buster also had reservations about the media",
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}
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for lang in LANG_MAP.keys():
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@ -434,7 +434,7 @@ class Wav2Vec2ProcessorWithLMTest(unittest.TestCase):
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def test_word_time_stamp_integration(self):
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import torch
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ds = load_dataset("common_voice", "en", split="train", streaming=True)
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ds = load_dataset("mozilla-foundation/common_voice_11_0", "en", split="train", streaming=True)
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ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
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ds_iter = iter(ds)
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sample = next(ds_iter)
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@ -442,7 +442,6 @@ class Wav2Vec2ProcessorWithLMTest(unittest.TestCase):
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processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
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model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
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# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
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input_values = processor(sample["audio"]["array"], return_tensors="pt").input_values
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with torch.no_grad():
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@ -461,6 +460,7 @@ class Wav2Vec2ProcessorWithLMTest(unittest.TestCase):
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]
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EXPECTED_TEXT = "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"
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EXPECTED_TEXT = "THE TRACK APPEARS ON THE COMPILATION ALBUM CRAFT FORKS"
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# output words
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self.assertEqual(" ".join(self.get_from_offsets(word_time_stamps, "word")), EXPECTED_TEXT)
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@ -471,8 +471,8 @@ class Wav2Vec2ProcessorWithLMTest(unittest.TestCase):
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end_times = torch.tensor(self.get_from_offsets(word_time_stamps, "end_time"))
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# fmt: off
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expected_start_tensor = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599])
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expected_end_tensor = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94])
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expected_start_tensor = torch.tensor([0.6800, 0.8800, 1.1800, 1.8600, 1.9600, 2.1000, 3.0000, 3.5600, 3.9800])
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expected_end_tensor = torch.tensor([0.7800, 1.1000, 1.6600, 1.9200, 2.0400, 2.8000, 3.3000, 3.8800, 4.2800])
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# fmt: on
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self.assertTrue(torch.allclose(start_times, expected_start_tensor, atol=0.01))
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