bayartsogt
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db90892
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Parent(s):
bb9a3e9
Small changes on header
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README.md
CHANGED
@@ -17,7 +17,7 @@ results:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice mn
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type: common_voice
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args: mn
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metrics:
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@@ -51,15 +51,15 @@ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def speech_file_to_array_fn(batch):
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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predicted_ids = torch.argmax(logits, dim=-1)
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@@ -86,31 +86,31 @@ processor = Wav2Vec2Processor.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mo
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model = Wav2Vec2ForCTC.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mongolian")
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model.to("cuda")
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chars_to_ignore_regex = '[
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def speech_file_to_array_fn(batch):
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def evaluate(batch):
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pred_ids = torch.argmax(logits, dim=-1)
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result = test_dataset.map(evaluate, batched=True, batch_size=8)
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice mn-MN
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type: common_voice
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args: mn
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metrics:
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def speech_file_to_array_fn(batch):
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\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
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\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
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\treturn batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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model = Wav2Vec2ForCTC.from_pretrained("bayartsogt/wav2vec2-large-xlsr-mongolian")
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model.to("cuda")
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chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\'h\\«\\»]'
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def speech_file_to_array_fn(batch):
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\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
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\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
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\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
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\treturn batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def evaluate(batch):
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\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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\twith torch.no_grad():
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\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
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pred_ids = torch.argmax(logits, dim=-1)
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\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
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\treturn batch
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result = test_dataset.map(evaluate, batched=True, batch_size=8)
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