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| import os | |
| os.system("pip install gradio==3.3") | |
| import gradio as gr | |
| import numpy as np | |
| import streamlit as st | |
| from audio_pipe import SpeechToSpeechPipeline | |
| title = "SpeechMatrix Speech-to-speech Translation" | |
| description = "Gradio Demo for SpeechMatrix. To use it, simply record your audio, or click the example to load. Read more at the links below. \nNote: These models are trained on SpeechMatrix data only, and meant to serve as a baseline for future research." | |
| article = "<p style='text-align: center'><a href='https://research.facebook.com/publications/speechmatrix' target='_blank'>SpeechMatrix</a> | <a href='https://github.com/facebookresearch/fairseq/tree/ust' target='_blank'>Github Repo</a></p>" | |
| SRC_LIST = ['cs', 'de', 'en', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl'] | |
| # SRC_LIST = ['cs', 'de', 'en', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl'] | |
| TGT_LIST = ['en', 'fr', 'es'] | |
| MODEL_LIST = ['xm_transformer_sm_all-en'] | |
| for src in SRC_LIST: | |
| for tgt in TGT_LIST: | |
| if src != tgt: | |
| MODEL_LIST.append(f"textless_sm_{src}_{tgt}") | |
| examples = [] | |
| pipe_dict = {} | |
| # io_dict = {model: gr.Interface.load(f"huggingface/facebook/{model}", api_key=st.secrets["api_key"]) for model in MODEL_LIST} | |
| # pipe_dict = {model: SpeechToSpeechPipeline(f"facebook/{model}") for model in MODEL_LIST} | |
| for model in MODEL_LIST: | |
| print(f"model: {model}") | |
| pipe_dict[model] = SpeechToSpeechPipeline(f"facebook/{model}") | |
| def inference(audio, model): | |
| out_audio = pipe_dict[model](audio).get_config()["value"]["name"] | |
| # pipe = SpeechToSpeechPipeline(f"facebook/{model}") | |
| # out_audio = pipe(audio).get_config()["value"]["name"] | |
| return out_audio | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Audio(source="microphone", type="filepath", label="Input"),gr.inputs.Dropdown(choices=MODEL_LIST, default="xm_transformer_sm_all-en",type="value", label="Model") | |
| ], | |
| gr.outputs.Audio(label="Output"), | |
| article=article, | |
| title=title, | |
| examples=examples, | |
| cache_examples=False, | |
| description=description).queue().launch() |