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--- |
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license: apache-2.0 |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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language: |
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- uz |
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metrics: |
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- wer |
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base_model: facebook/wav2vec2-base-960h |
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pipeline_tag: automatic-speech-recognition |
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library_name: adapter-transformers |
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--- |
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# Author |
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Mamayusupov Rifat. |
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# Usage |
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``` |
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from transformers import SeamlessM4TFeatureExtractor, Wav2Vec2BertProcessor, Wav2Vec2CTCTokenizer, Wav2Vec2BertForCTC |
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from transformers import pipeline |
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# Initialize tokenizer |
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tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("/home/rifat/asr", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|") |
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# Initialize feature extractor |
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feature_extractor = SeamlessM4TFeatureExtractor(feature_size=80, num_mel_bins=80, sampling_rate=16000, padding_value=0.0) |
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# Initialize processor |
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processor = Wav2Vec2BertProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) |
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# Initialize model |
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model = Wav2Vec2BertForCTC.from_pretrained( |
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args.pretrained_model, |
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attention_dropout=0.0, |
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hidden_dropout=0.0, |
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feat_proj_dropout=0.0, |
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mask_time_prob=0.0, |
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layerdrop=0.0, |
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ctc_loss_reduction="mean", |
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add_adapter=True, |
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pad_token_id=processor.tokenizer.pad_token_id, |
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vocab_size=len(processor.tokenizer), |
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ignore_mismatched_sizes=True |
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) |
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model.config.ctc_zero_infinity = True |
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model.to("cuda") |
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# Perform inference |
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# Initialize the pipeline |
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pipe = pipeline(model=model, tokenizer=processor.tokenizer, feature_extractor=feature_extractor, task="automatic-speech-recognition") |
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input_audio = "" |
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print(pipe(input_audio)['result_text']) |
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``` |
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