[MMS] Scaling Speech Technology to 1,000+ Languages | Add attention adapter to Wav2Vec2 (#23813)
* add fine-tuned with adapter layer * Add set_target_lang to tokenizer * Implement load adapter * add tests * make style * Apply suggestions from code review * Update src/transformers/models/wav2vec2/tokenization_wav2vec2.py * make fix-copies * Apply suggestions from code review * make fix-copies * make style again * mkae style again * fix doc string * Update tests/models/wav2vec2/test_tokenization_wav2vec2.py * Apply suggestions from code review * fix * Correct wav2vec2 adapter * mkae style * Update src/transformers/models/wav2vec2/modeling_wav2vec2.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * add more nice docs * finish * finish * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Apply suggestions from code review * all finish --------- Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
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Patrick von Platen
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GitHub
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@@ -54,6 +54,7 @@ from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from safetensors.torch import save_file as safe_save_file
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from transformers import (
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Wav2Vec2FeatureExtractor,
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@@ -67,6 +68,8 @@ if is_torch_available():
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Wav2Vec2Processor,
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)
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from transformers.models.wav2vec2.modeling_wav2vec2 import (
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WAV2VEC2_ADAPTER_PT_FILE,
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WAV2VEC2_ADAPTER_SAFE_FILE,
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Wav2Vec2GumbelVectorQuantizer,
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_compute_mask_indices,
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_sample_negative_indices,
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@@ -290,6 +293,17 @@ class Wav2Vec2ModelTester:
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(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
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)
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def create_and_check_model_with_attn_adapter(self, config, input_values, attention_mask):
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config.adapter_attn_dim = 16
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model = Wav2Vec2ForCTC(config=config)
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self.parent.assertIsNotNone(model._adapters)
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model.to(torch_device)
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model.eval()
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result = model(input_values, attention_mask=attention_mask)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.output_seq_length, self.vocab_size))
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def create_and_check_batch_inference(self, config, input_values, *args):
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# test does not pass for models making use of `group_norm`
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# check: https://github.com/pytorch/fairseq/issues/3227
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@@ -844,6 +858,10 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
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def test_model_with_attn_adapter(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_with_attn_adapter(*config_and_inputs)
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def test_batched_inference(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_batch_inference(*config_and_inputs)
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@@ -1098,6 +1116,85 @@ class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
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def test_feed_forward_chunking(self):
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pass
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def test_load_attn_adapter(self):
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processor = Wav2Vec2Processor.from_pretrained(
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"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
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)
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def get_logits(model, input_features):
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model = model.to(torch_device)
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batch = processor(
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input_features,
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padding=True,
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sampling_rate=processor.feature_extractor.sampling_rate,
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return_tensors="pt",
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)
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with torch.no_grad():
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logits = model(
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input_values=batch["input_values"].to(torch_device),
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attention_mask=batch["attention_mask"].to(torch_device),
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).logits
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return logits
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input_features = [np.random.random(16_000 * s) for s in [1, 3, 2, 6]]
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model = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", adapter_attn_dim=16)
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with tempfile.TemporaryDirectory() as tempdir:
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model.save_pretrained(tempdir)
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model = Wav2Vec2ForCTC.from_pretrained(tempdir)
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logits = get_logits(model, input_features)
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adapter_weights = model._adapters
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# save safe weights
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safe_filepath = os.path.join(tempdir, WAV2VEC2_ADAPTER_SAFE_FILE.format("eng"))
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safe_save_file(adapter_weights, safe_filepath, metadata={"format": "pt"})
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model.load_adapter("eng")
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model.load_adapter("eng", use_safetensors=True)
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with self.assertRaises(OSError):
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model.load_adapter("eng", use_safetensors=False)
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with self.assertRaises(Exception):
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model.load_adapter("ita", use_safetensors=True)
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logits_2 = get_logits(model, input_features)
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self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
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with tempfile.TemporaryDirectory() as tempdir:
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model.save_pretrained(tempdir)
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model = Wav2Vec2ForCTC.from_pretrained(tempdir)
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logits = get_logits(model, input_features)
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adapter_weights = model._adapters
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# save pt weights
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pt_filepath = os.path.join(tempdir, WAV2VEC2_ADAPTER_PT_FILE.format("eng"))
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torch.save(adapter_weights, pt_filepath)
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model.load_adapter("eng")
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model.load_adapter("eng", use_safetensors=False)
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with self.assertRaises(OSError):
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model.load_adapter("eng", use_safetensors=True)
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logits_2 = get_logits(model, input_features)
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self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
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model = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter")
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logits = get_logits(model, input_features)
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model.load_adapter("eng")
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model.load_adapter("eng", use_safetensors=False)
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model.load_adapter("eng", use_safetensors=True)
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logits_2 = get_logits(model, input_features)
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self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
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@slow
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def test_model_from_pretrained(self):
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model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
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@@ -1768,3 +1865,45 @@ class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
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# TODO: update the tolerance after the CI moves to torch 1.10
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self.assertAlmostEqual(outputs.loss.item(), 17.7963, 2)
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@require_torchaudio
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def test_inference_mms_1b_all(self):
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model = Wav2Vec2ForCTC.from_pretrained("facebook/mms-1b-all").to(torch_device)
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processor = Wav2Vec2Processor.from_pretrained("facebook/mms-1b-all")
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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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sample = next(iter(ds))
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wav2vec2_lang = LANG_MAP[lang]
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model.load_adapter(wav2vec2_lang)
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processor.tokenizer.set_target_lang(wav2vec2_lang)
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resampled_audio = torchaudio.functional.resample(
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torch.tensor(sample["audio"]["array"]), 48_000, 16_000
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).numpy()
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inputs = processor(resampled_audio, sampling_rate=16_000, return_tensors="pt")
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input_values = inputs.input_values.to(torch_device)
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attention_mask = inputs.attention_mask.to(torch_device)
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with torch.no_grad():
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outputs = model(input_values, attention_mask=attention_mask).logits
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ids = torch.argmax(outputs, dim=-1)[0]
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transcription = processor.decode(ids)
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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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}
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for lang in LANG_MAP.keys():
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assert run_model(lang) == TRANSCRIPTIONS[lang]
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