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import jax.numpy as jnp |
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from transformers import AutoFeatureExtractor, AutoTokenizer |
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from models.modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel |
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encoder_id = "hf-internal-testing/tiny-random-wav2vec2" |
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decoder_id = "hf-internal-testing/tiny-random-bart" |
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model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained( |
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encoder_id, decoder_id, encoder_from_pt=True, decoder_from_pt=True, encoder_add_adapter=True |
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) |
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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.config.decoder.bos_token_id |
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model.config.pad_token_id = model.config.decoder.pad_token_id |
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model.config.eos_token_id = model.config.decoder.eos_token_id |
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model.config.max_length = 20 |
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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.processor_class = "Wav2Vec2Processor" |
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out = model.generate(jnp.ones((1, 2000))) |
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model.save_pretrained("./") |
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feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id) |
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feature_extractor.save_pretrained("./") |
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tokenizer = AutoTokenizer.from_pretrained(decoder_id) |
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tokenizer.save_pretrained("./") |
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