Update README.md
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README.md
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---
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language: ml
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datasets:
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metrics:
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- wer
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tags:
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@@ -53,15 +53,15 @@ resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the audio 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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@@ -90,33 +90,33 @@ processor = Wav2Vec2Processor.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam
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model = Wav2Vec2ForCTC.from_pretrained("gvs/wav2vec2-large-xlsr-malayalam")
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model.to("cuda")
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chars_to_ignore_regex = '[
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unicode_ignore_regex = r'[
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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 audio files as arrays
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def speech_file_to_array_fn(batch):
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batch["sentence"] = re.sub(unicode_ignore_regex, '', batch["sentence"])
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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 audio files as arrays
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def evaluate(batch):
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result = test_dataset.map(evaluate, batched=True, batch_size=8)
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---
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language: ml
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datasets:
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- Indic TTS Malayalam Speech Corpus
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- Openslr Malayalam Speech Corpus
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- SMC Malayalam Speech Corpus
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metrics:
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- wer
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tags:
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# Preprocessing the datasets.
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# We need to read the audio 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("gvs/wav2vec2-large-xlsr-malayalam")
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model.to("cuda")
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chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�Utrnle\\_]'
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unicode_ignore_regex = r'[\\u200d\\u200c\\u200e]'
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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 audio 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"])
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batch["sentence"] = re.sub(unicode_ignore_regex, '', batch["sentence"])
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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 audio 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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\tpred_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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