Spaces:

facebook
/

xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022

Build error

App Files Files Community

xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022 / app.py

lpw

Update app.py

5cfb66e over 1 year ago

raw history blame contribute delete

No virus

1.64 kB

	import os
	os.system("pip install gradio==3.3")
	import gradio as gr
	import numpy as np
	import streamlit as st

	title = "Fairseq Speech to Speech Translation"

	description = "Gradio Demo for fairseq S2S: speech-to-speech translation models. To use it, simply record your audio, or click the example to load. Read more at the links below."

	article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.05604' target='_blank'>Direct speech-to-speech translation with discrete units</a> \| <a href='https://github.com/facebookresearch/fairseq/tree/main/examples/speech_to_speech' target='_blank'>Github Repo</a></p>"

	examples = [
	["enhanced_direct_s2st_units_audios_es-en_set2_source_12478_cv.flac","xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022"],
	]

	io1 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022", api_key=st.secrets["api_key"])

	def inference(audio, model):
	# if mic is not None and file is None:
	# audio = mic
	# elif file is not None and mic is None:
	# audio = file
	# else:
	# return "ERROR: You must and may only select one method, it cannot be empty or select both methods at once."
	out_audio = io1(audio)
	return out_audio


	gr.Interface(
	inference,
	[gr.inputs.Audio(source="microphone", type="filepath", label="Input"),gr.inputs.Dropdown(choices=["xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022"], default="xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022",type="value", label="Model")
	],
	gr.outputs.Audio(label="Output"),
	article=article,
	title=title,
	description=description).queue().launch()