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from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer |
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import torch |
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encoder_id = "facebook/wav2vec2-large-lv60" |
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decoder_id = "facebook/bart-large" |
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model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True) |
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model.config.encoder.feat_proj_dropout = 0.0 |
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model.config.encoder.final_dropout = 0.0 |
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model.config.encoder.mask_time_prob = 0.1 |
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model.config.decoder_start_token_id = model.decoder.config.bos_token_id |
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model.config.pad_token_id = model.decoder.config.pad_token_id |
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model.config.eos_token_id = model.decoder.config.eos_token_id |
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model.config.max_length = 40 |
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model.config.num_beams = 1 |
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model.config.encoder.layerdrop = 0.0 |
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model.config.use_cache = False |
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model.config.decoder.use_cache = False |
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model.config.processor_class = "Wav2Vec2Processor" |
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out = model.generate(torch.ones((1, 2000))) |
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model.save_pretrained("./") |
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feature_etxractor = AutoFeatureExtractor.from_pretrained(encoder_id) |
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feature_etxractor.save_pretrained("./") |
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tokenizer = AutoTokenizer.from_pretrained(decoder_id) |
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tokenizer.save_pretrained("./") |
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