cromz22
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wav2vec2-2-bart-base

Automatic Speech Recognition

speech-encoder-decoder

Inference Endpoints

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wav2vec2-2-bart-base / load_and_save.py

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	#!/usr/bin/env python3

	# from transformers.utils.dummy_pt_objects import SpeechEncoderDecoderModel
	# from transformers.models.auto.feature_extraction_auto import AutoFeatureExtractor
	# from transformers.models.auto.tokenization_auto import AutoTokenizer
	# from transformers.models.wav2vec2.processing_wav2vec2 import Wav2Vec2Processor

	from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2Processor


	# checkpoints to leverage
	encoder_id = "facebook/wav2vec2-base"
	decoder_id = "facebook/bart-base"

	# load and save speech-encoder-decoder model
	# set some hyper-parameters for training and evaluation
	model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
	encoder_id,
	decoder_id,
	encoder_add_adapter=True,
	encoder_feat_proj_dropout=0.0,
	encoder_layerdrop=0.0,
	max_length=200,
	num_beams=5,
	)
	model.config.decoder_start_token_id = model.decoder.config.bos_token_id
	model.config.pad_token_id = model.decoder.config.pad_token_id
	model.config.eos_token_id = model.decoder.config.eos_token_id
	model.save_pretrained("./")

	# load and save processor
	feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
	tokenizer = AutoTokenizer.from_pretrained(decoder_id)
	processor = Wav2Vec2Processor(feature_extractor, tokenizer)
	processor.save_pretrained("./")