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from transformers import pipeline
import os
import gradio as gr
import torch
from IPython.display import Audio as IPythonAudio
#Audio to text
asr = pipeline(task="automatic-speech-recognition",
model="distil-whisper/distil-small.en")
#Text to text
translator = pipeline(task="translation",
model="facebook/nllb-200-distilled-600M",
torch_dtype=torch.bfloat16)
#Text to audio
pipe = pipeline("text-to-speech", model="suno/bark-small")
demo = gr.Blocks()
def transcribe_speech(filepath):
if filepath is None:
gr.Warning("No audio found, please retry.")
return ""
output = translator(asr(filepath)["text"],
src_lang="eng_Latn",
tgt_lang="hin_Deva")
narrated_text=pipe(output[0]['translation_text'])
output=IPythonAudio(narrated_text["audio"][0],
rate=narrated_text["sampling_rate"])
return output
mic_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="microphone",
type="filepath"),
outputs=gr.Audio(label="Translated Message"),
allow_flagging="never")
file_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="upload",
type="filepath"),
outputs=gr.Audio(label="Translated Message"),
allow_flagging="never",
)
with demo:
gr.TabbedInterface(
[mic_transcribe,
file_transcribe],
["Transcribe Microphone",
"Transcribe Audio File"],
)
demo.launch(share=True)
demo.close() |