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import torch
from transformers import pipeline
from transformers import VitsModel, VitsTokenizer
import numpy as np
import gradio as gr
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-base",
device=device
)
def translate(audio):
outputs = pipe(
audio,
max_new_tokens=256,
generate_kwargs={"task": "transcribe", "language": "es"}
)
model = VitsModel.from_pretrained("facebook/mms-tts-spa")
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-spa")
def synthesise(text):
inputs=tokenizer(text, return_tensors="pt")
input_ids = inputs["input_ids"]
with torch.no_grad():
outputs = model(input_ids)
return outputs["waveform"]
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
demo = gr.Blocks()
mic_translate = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(sources="microphone", type="filepath"),
outputs=gr.Audio(label="Generated Speech", type="numpy"),
)
file_translate = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(sources="upload", type="filepath"),
outputs=gr.Audio(label="Generated Speech", type="numpy"),
)
with demo:
gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
demo.launch() |