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import gradio as gr
import torch
import scipy.io.wavfile as wavfile
from transformers import AutoProcessor, SeamlessM4TModel, pipeline
# tokenizer = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-medium")
# model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-medium")
# text = "some example text in the English language"
# def greet(text):
# inputs = tokenizer(text, return_tensors="pt")
# with torch.no_grad():
# output = model(**inputs, decoder_input_ids=inputs["input_ids"]).waveform
# out = output[0]
# wavfile.write("tmp.wav", rate=16000, data=out)
# return open("tmp.wav", "rb").read()
def stt(audio):
print(audio)
br, data = audio
tscrb = pipeline("automatic-speech-recognition", model="facebook/hubert-large-ls960-ft")
return tscrb(data)
iface = gr.Interface(fn=stt, inputs="audio", outputs="text")
iface.launch() |