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| import os | |
| os.system('pip install gradio --upgrade') | |
| os.system('pip freeze') | |
| import time | |
| import torch | |
| import string | |
| from espnet_model_zoo.downloader import ModelDownloader | |
| from espnet2.bin.asr_inference import Speech2Text | |
| import soundfile | |
| import librosa | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| def text_normalizer(text): | |
| text = text.upper() | |
| return text.translate(str.maketrans('', '', string.punctuation)) | |
| def inference(audio, model): | |
| lang = 'multilingual' | |
| fs = 16000 | |
| tag = model | |
| 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 | |
| ) | |
| speech, rate = librosa.load(audio.name, sr=16000) | |
| 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"),gr.inputs.Dropdown(choices=["ftshijt/open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave_10best","Shinji Watanabe/spgispeech_asr_train_asr_conformer6_n_fft512_hop_length256_raw_en_unnorm_bpe5000_valid.acc.ave"], type="value", default="ftshijt/open_li52_asr_train_asr_raw_bpe7000_valid.acc.ave_10best", label="model")] | |
| outputs = gr.outputs.Textbox(label="Output Text") | |
| title = "ESPnet2-ASR" | |
| 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 = "<p style='text-align: center'><a href='https://espnet.github.io/espnet/'>ESPnet: end-to-end speech processing toolkit</a> | <a href='https://github.com/espnet/espnet'>Github Repo</a></p>" | |
| examples = [ | |
| ["poem.wav"] | |
| ] | |
| gr.Interface(inference, inputs, outputs, title=title, description=description, article=article,examples=examples, enable_queue=True).launch() | |