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| '''Librispeech 100h English ASR demo | |
| @ML2 --> @HuggingFace | |
| 2022-02-23 jkang first created | |
| 2022-02-28 jkang char added (bpe, word, char now all included) | |
| ''' | |
| import os | |
| from difflib import Differ | |
| from glob import glob | |
| from loguru import logger | |
| import librosa | |
| import gradio as gr | |
| from espnet2.bin.asr_inference import Speech2Text | |
| # ---------- Settings ---------- | |
| GPU_ID = '-1' | |
| os.environ['CUDA_VISIBLE_DEVICES'] = GPU_ID | |
| DEVICE = 'cuda' if GPU_ID != '-1' else 'cpu' | |
| # SERVER_PORT = 42208 | |
| # SERVER_NAME = "0.0.0.0" | |
| MODEL_DIR = './model' | |
| EXAMPLE_DIR = './examples' | |
| examples = sorted(glob(os.path.join(EXAMPLE_DIR, '*.wav'))) | |
| # examples = [[example] for example in examples] | |
| d = Differ() | |
| # ---------- Logging ---------- | |
| logger.add('app.log', mode='a') | |
| logger.info('============================= App restarted =============================') | |
| # ---------- Model ---------- | |
| logger.info('download model') | |
| logger.info('model downloaded') | |
| model_word = Speech2Text.from_pretrained("jkang/espnet2_librispeech_100_conformer_word") | |
| model_bpe = Speech2Text.from_pretrained("jkang/espnet2_librispeech_100_conformer") | |
| model_char = Speech2Text.from_pretrained("jkang/espnet2_librispeech_100_conformer_char") | |
| logger.info('model loaded') | |
| def predict(wav_file): | |
| logger.info('wav file loaded') | |
| # Load audio | |
| speech, rate = librosa.load(wav_file, sr=16000) | |
| logger.info(f'--- speech.shape: {speech.shape}') | |
| logger.info(f'--- speech.dtype: {speech.dtype}') | |
| logger.info(f'--- speech rate: {rate}') | |
| # Run inference | |
| W = model_word(speech)[0] | |
| B = model_bpe(speech)[0] | |
| C = model_char(speech)[0] | |
| word_decoded = W[0] | |
| bpe_decoded = B[0] | |
| char_decoded = C[0] | |
| # comparison = ''.join(list(d.compare([word_decoded+'\n'], [bpe_decoded+'\n']))) | |
| logger.info(f'--- word-based model decoded: {word_decoded}') | |
| logger.info(f'--- BPE-based model decoded: {bpe_decoded}') | |
| logger.info(f'--- char-based model decoded: {char_decoded}') | |
| logger.info('finished') | |
| # return word_decoded, bpe_decoded, comparison | |
| return bpe_decoded, char_decoded, word_decoded | |
| iface = gr.Interface( | |
| predict, | |
| title='Comparison between word vs BPE tokens based on ESPNet2 ASR models', | |
| description='Two models were trained on Librispeech (clean-100h)', | |
| inputs=[ | |
| gr.inputs.Audio(label='wav file', source='upload', type='filepath') | |
| ], | |
| outputs=[ | |
| gr.outputs.Textbox(label='Decoding result (BPE 기반 model)'), | |
| gr.outputs.Textbox(label='Decoding result (char 기반 model)'), | |
| gr.outputs.Textbox(label='Decoding result (word 기반 model)'), | |
| # gr.outputs.Textbox(label='Comparison'), | |
| ], | |
| examples=examples, | |
| examples_per_page=5, | |
| css='.examples-gallery {display: grid !important;}' | |
| # article='<p style="text-align:center">Model URL<a target="_blank" href="https://huggingface.co/jkang/espnet2_librispeech_100_conformer">🤗</a></p>', | |
| ) | |
| if __name__ == '__main__': | |
| try: | |
| iface.launch(debug=True, | |
| # server_name=SERVER_NAME, | |
| # server_port=SERVER_PORT, | |
| enable_queue=True, | |
| ) | |
| except KeyboardInterrupt as e: | |
| print(e) | |
| finally: | |
| iface.close() |