Update README.md
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README.md
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@@ -43,7 +43,7 @@ import torchaudio
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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test_dataset = <load-test-split-of-combined-dataset> #
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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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@@ -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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@@ -81,8 +81,39 @@ import torchaudio
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from datasets import load_dataset, load_metric
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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import re
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wer = load_metric("wer")
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@@ -90,33 +121,33 @@ 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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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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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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test_dataset = <load-test-split-of-combined-dataset> # Details on loading this dataset in the evaluation section
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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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# 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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speech_array, sampling_rate = torchaudio.load(batch["path"])
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batch["speech"] = resampler(speech_array).squeeze().numpy()
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return 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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logits = 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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from datasets import load_dataset, load_metric
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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import re
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from datasets import load_dataset, load_metric
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from pathlib import Path
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data_dir = Path('<path-to-custom-dataset>')
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dataset_folders = {
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'openslr': 'openslr',
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'indic-tts': 'indic-tts-ml',
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}
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# Set directories for datasets
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openslr_male_dir = data_dir / dataset_folders['openslr'] / 'male'
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openslr_female_dir = data_dir / dataset_folders['openslr'] / 'female'
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indic_tts_male_dir = data_dir / dataset_folders['indic-tts'] / 'male'
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indic_tts_female_dir = data_dir / dataset_folders['indic-tts'] / 'female'
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# Load the datasets, total count is set manually
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openslr_male = load_dataset("json", data_files=[f"{str(openslr_male_dir.absolute())}/sample_{i}.json" for i in range(2023)], split="train")
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openslr_female = load_dataset("json", data_files=[f"{str(openslr_female_dir.absolute())}/sample_{i}.json" for i in range(2103)], split="train")
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indic_tts_male = load_dataset("json", data_files=[f"{str(indic_tts_male_dir.absolute())}/sample_{i}.json" for i in range(5649)], split="train")
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indic_tts_female = load_dataset("json", data_files=[f"{str(indic_tts_female_dir.absolute())}/sample_{i}.json" for i in range(2950)], split="train")
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# Create test split as 20%, set random seed as well.
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test_size = 0.2
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random_seed=1
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openslr_male_splits = openslr_male.train_test_split(test_size=test_size, seed=random_seed)
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openslr_female_splits = openslr_female.train_test_split(test_size=test_size, seed=random_seed)
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indic_tts_male_splits = indic_tts_male.train_test_split(test_size=test_size, seed=random_seed)
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indic_tts_female_splits = indic_tts_female.train_test_split(test_size=test_size, seed=random_seed)
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# Get combined test dataset
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split_list = [openslr_male_splits, openslr_female_splits, indic_tts_male_splits, indic_tts_female_splits]
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test_dataset = datasets.concatenate_datasets([split['test'] for split in split_list)
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wer = load_metric("wer")
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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'[\u200c\u200d\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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batch["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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speech_array, sampling_rate = torchaudio.load(batch["path"])
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batch["speech"] = resampler(speech_array).squeeze().numpy()
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return 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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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = 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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batch["pred_strings"] = processor.batch_decode(pred_ids)
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return batch
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result = test_dataset.map(evaluate, batched=True, batch_size=8)
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