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import gradio as gr
import numpy as np
import torch
from datasets import load_dataset
from transformers import VitsModel, VitsTokenizer, pipeline
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
# load text-to-speech checkpoint
tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu")
model = VitsModel.from_pretrained("Matthijs/mms-tts-deu")
model.to(device)
def translate(audio):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "german"})
return outputs["text"]
def synthesize(text):
input = tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = model(input['input_ids'].to(device))
return output.audio[0].cpu()
target_dtype = np.int16 # output audio file format expected by Gradio
max_range = np.iinfo(target_dtype).max
def speech_to_speech_translation(audio):
translated_text = translate(audio)
synthesized_speech = synthesize(translated_text)
# normalize audio array by dynamic range of target dtype for Gradio
synthesized_speech = (synthesized_speech.numpy() * max_range).astype(target_dtype)
return 16000, synthesized_speech
title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in German. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Facebook's
[MMS](https://huggingface.co/facebook/mms-tts) model for text-to-speech:
"""
demo = gr.Blocks()
mic_translate = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(source="microphone", type="filepath"),
outputs=gr.Audio(label="Generated Speech", type="numpy"),
title=title,
description=description,
)
file_translate = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(source="upload", type="filepath"),
outputs=gr.Audio(label="Generated Speech", type="numpy"),
title=title,
description=description,
)
with demo:
gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
demo.launch(debug=True)
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