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import gradio as gr
import librosa
import logging
import numpy as np
import torch
from transformers import VitsModel, VitsTokenizer, pipeline
from transformers import WhisperForConditionalGeneration, WhisperProcessor
device = "cuda:0" if torch.cuda.is_available() else "cpu"
target_language = "fr"
# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="bofenghuang/whisper-small-cv11-french", device=device)
# whisper_model_name = "openai/whisper-small"
# whisper_processor = WhisperProcessor.from_pretrained(whisper_model_name)
# whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_name)
# decoder_ids = whisper_processor.get_decoder_prompt_ids(language=target_language, task="transcribe")
# load text-to-speech checkpoint
model = VitsModel.from_pretrained("facebook/mms-tts-fra")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra")
def translate(audio):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": target_language})
return outputs["text"]
# def translate(audio):
# if isinstance(audio, str):
# # Account for recorded audio
# audio = {
# "path": audio,
# "sampling_rate": 16_000,
# "array": librosa.load(audio, sr=16_000)[0]
# }
# elif audio["sampling_rate"] != 16_000:
# audio["array"] = librosa.resample(audio["array"], audio["sampling_rate"], 16_000)
# input_features = whisper_processor(audio["array"], sampling_rate=16000, return_tensors="pt").input_features
# predicted_ids = whisper_model.generate(input_features, forced_decoder_ids=decoder_ids)
# translated_text = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
# return translated_text
def synthesise(text):
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
outputs = model(inputs["input_ids"])
speech = outputs["waveform"][0]
logging.info(speech)
return speech.cpu()
def speech_to_speech_translation(audio):
translated_text = translate(audio)
logging.info(f"Translated Text: {translated_text}")
synthesised_speech = synthesise(translated_text)
synthesised_speech = (synthesised_speech.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, and Microsoft's
[SpeechT5 TTS](https://huggingface.co/preetam8/speecht5_finetuned_voxpopuli_fr) model for text-to-speech finetuned for french:
"""
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"])
logging.getLogger().setLevel(logging.INFO)
demo.launch() |