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import gradio as gr
import numpy as np
import torch
from datasets import load_dataset
from transformers import 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)
def translate(audio):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
return outputs["text"]
'''
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
# load text-to-speech checkpoint and speaker embeddings
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
def synthesise_old(text):
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
return speech.cpu()
def speech_to_speech_translation_old(audio):
translated_text = translate(audio)
synthesised_speech = synthesise_old(translated_text)
synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
return 16000, synthesised_speech
'''
from transformers import VitsModel, VitsTokenizer
# load translator to french
en_fr_translator = pipeline("translation_en_to_fr")
# load text-to-speech
model_new = VitsModel.from_pretrained("facebook/mms-tts-fra")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")
def synthesise(text):
translation_to_french = en_fr_translator(text)
french_text = translation_to_french[0]['translation_text']
inputs = tokenizer(french_text, return_tensors="pt")
input_ids = inputs["input_ids"]
with torch.no_grad():
outputs = model_new(input_ids)
speech = outputs["waveform"]
return speech
def speech_to_speech_translation(audio):
translated_text = translate(audio)
synthesised_speech = synthesise(translated_text)
synthesised_speech = (synthesised_speech[0].numpy() * 32767).astype(np.int16)
return 16000, synthesised_speech
title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in French.
Demo uses OpenAI's [Whisper Base](https://huggingface.co./openai/whisper-base) model for speech translation,
Google's [T5](https://huggingface.co./t5-base) for translating from English to French
and Facebook's [Massive Multilingual Speech (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,
api_name='predict',
)
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,
api_name='predict_upload',
)
with demo:
gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
demo.queue()
demo.launch()
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