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import gradio as gr
import numpy as np
import torch
from datasets import load_dataset
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# Load Whisper large-v2 model for multilingual speech translation
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device)
# Load MMS TTS model for multilingual text-to-speech (using German model as base)
processor = SpeechT5Processor.from_pretrained("facebook/s2t-medium-mustc-multilingual-st")
model = SpeechT5ForTextToSpeech.from_pretrained("facebook/s2t-medium-mustc-multilingual-st").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("facebook/s2t-medium-mustc-multilingual-st").to(device)
# Define supported languages (adjust based on the languages supported by the model)
LANGUAGES = {
"German": "deu", "English": "eng", "French": "fra", "Spanish": "spa",
"Italian": "ita", "Portuguese": "por", "Polish": "pol", "Turkish": "tur"
}
def translate(audio, source_lang, target_lang):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={
"task": "transcribe",
"language": source_lang,
})
transcription = outputs["text"]
# Use Whisper for translation
translation = asr_pipe(transcription, max_new_tokens=256, generate_kwargs={
"task": "translate",
"language": target_lang,
})["text"]
return translation
def synthesise(text, target_lang):
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(inputs["input_ids"].to(device), vocoder=vocoder, language=LANGUAGES[target_lang])
return speech.cpu()
def speech_to_speech_translation(audio, source_lang, target_lang):
translated_text = translate(audio, LANGUAGES[source_lang], LANGUAGES[target_lang])
synthesised_speech = synthesise(translated_text, target_lang)
synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
return 16000, synthesised_speech
title = "Multilingual Speech-to-Speech Translation"
description = """
Demo for multilingual speech-to-speech translation (STST), mapping from source speech in any supported language to target speech in any other supported language.
"""
demo = gr.Blocks()
with demo:
gr.Markdown(f"# {title}")
gr.Markdown(description)
with gr.Row():
source_lang = gr.Dropdown(choices=list(LANGUAGES.keys()), label="Source Language")
target_lang = gr.Dropdown(choices=list(LANGUAGES.keys()), label="Target Language")
with gr.Tabs():
with gr.TabItem("Microphone"):
mic_input = gr.Audio(source="microphone", type="filepath")
mic_output = gr.Audio(label="Generated Speech", type="numpy")
mic_button = gr.Button("Translate")
with gr.TabItem("Audio File"):
file_input = gr.Audio(source="upload", type="filepath")
file_output = gr.Audio(label="Generated Speech", type="numpy")
file_button = gr.Button("Translate")
mic_button.click(
speech_to_speech_translation,
inputs=[mic_input, source_lang, target_lang],
outputs=mic_output
)
file_button.click(
speech_to_speech_translation,
inputs=[file_input, source_lang, target_lang],
outputs=file_output
)
demo.launch() |