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import gradio as gr
import numpy as np
import torch
from transformers import pipeline, VitsModel, VitsTokenizer
device = "cuda:0" if torch.cuda.is_available() else "cpu"
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max
# load speech translation checkpoint
ASR_MODEL_NAME = 'openai/whisper-base'
asr_pipe = pipeline("automatic-speech-recognition", model=ASR_MODEL_NAME, device=device)
# load text-to-speech checkpoint
model = VitsModel.from_pretrained("Matthijs/mms-tts-deu")
tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu")
def translate(audio):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "de"})
return outputs["text"]
def synthesise(text):
inputs = tokenizer(text, return_tensors="pt")
input_ids = inputs["input_ids"]
with torch.no_grad():
outputs = model(input_ids)
speech = outputs.audio[0]
return speech.cpu()
def speech_to_speech_translation(audio):
translated_text = translate(audio)
synthesised_speech = synthesise(translated_text)
synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
return 16000, synthesised_speech
title = "Cascaded STST - Any language to German speech"
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 Microsoft's
[MMS TTS](https://huggingface.co/Matthijs/mms-tts-deu) 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"),
examples=[["./example.wav"]],
title=title,
description=description,
)
with demo:
gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
demo.queue(concurrency_count=2,max_size=10)
demo.launch() |