import time import torch import string from espnet_model_zoo.downloader import ModelDownloader from espnet2.bin.asr_inference import Speech2Text import soundfile import librosa.display import matplotlib.pyplot as plt import gradio as gr import wget url = 'https://github.com/AK391/fairseq/blob/master/poem.wav' wget.download(url) lang = 'multilingual' fs = 16000 tag = 'ftshijt/open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave_10best' d = ModelDownloader() speech2text = Speech2Text( **d.download_and_unpack(tag), device="cpu", minlenratio=0.0, maxlenratio=0.0, ctc_weight=0.3, beam_size=10, batch_size=0, nbest=1 ) def text_normalizer(text): text = text.upper() return text.translate(str.maketrans('', '', string.punctuation)) def inference(audio): speech, rate = soundfile.read(audio.name) assert rate == fs, "mismatch in sampling rate" nbests = speech2text(speech) text, *_ = nbests[0] return f"ASR hypothesis: {text_normalizer(text)}" inputs = gr.inputs.Audio(label="Input Audio", type="file") outputs = gr.outputs.Textbox(label="Output Text") title = "wav2vec 2.0" description = "Gradio demo for Real-time ASR with ESPnet2. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below." article = "
" examples = [ ["poem.wav"] ] gr.Interface(inference, inputs, outputs, title=title, description=description, article=article,examples=examples).launch()