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from transformers import pipeline
import os
import gradio as gr
import torch
#from IPython.display import Audio as IPythonAudio
#from gtts import gTTS
#import IPython.display as ipd
#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 ""
#asr(filepath)["text"]
output = translator(filepath,
src_lang="eng_Latn",
tgt_lang="hin_Deva")
narrated_text=pipe(output[0]['translation_text'])
#tts = gTTS(text=narrated_text, lang='hi', slow=False)
#tts.save("translated_audio.mp3")
#return ipd.Audio("translated_audio.mp3", autoplay=True)
return narrated_text
mic_transcribe = gr.Interface(
fn=transcribe_speech,
#inputs=gr.Audio(sources="microphone",
# type="filepath"),
inputs=gr.Textbox(label="text",lines=3),
outputs="audio",
#outputs=gr.Audio(label="Translated Message"),
allow_flagging="never")
file_transcribe = gr.Interface(
fn=transcribe_speech,
#inputs=gr.Audio(sources="upload",
# type="filepath"),
inputs=gr.Textbox(label="text",lines=3),
outputs="audio",
#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() |