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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'] | |
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"] | |
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() |