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from transformers import WhisperTokenizer
import os
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small") #, language="marathi", task="transcribe"
from transformers import pipeline
import gradio as gr
import torch
pipe = pipeline(model="thak123/gom-stt-v3", #"thak123/whisper-small-LDC-V1", #"thak123/whisper-small-gom",
task="automatic-speech-recognition", tokenizer= tokenizer) # change to "your-username/the-name-you-picked"
# pipe.model.config.forced_decoder_ids = (
# pipe.tokenizer.get_decoder_prompt_ids(
# language="marathi", task="transcribe"
# )
# )
def transcribe_speech(filepath):
output = pipe(
filepath,
max_new_tokens=256,
generate_kwargs={
"task": "transcribe",
"language": "konkani",
}, # update with the language you've fine-tuned on
chunk_length_s=30,
batch_size=8,
padding=True
)
return output["text"]
demo = gr.Blocks()
mic_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="microphone", type="filepath"),
outputs=gr.components.Textbox(),
)
file_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="upload", type="filepath"),
outputs=gr.components.Textbox(),
)
with demo:
gr.TabbedInterface(
[mic_transcribe, file_transcribe],
["Transcribe Microphone", "Transcribe Audio File"],
)
demo.launch(debug=True)
# def transcribe(audio):
# # text = pipe(audio)["text"]
# # pipe(audio)
# text = pipe(audio)
# print("op",text)
# return text#pipe(audio) #text
# iface = gr.Interface(
# fn=transcribe,
# inputs=[gr.Audio(sources=["microphone", "upload"])],
# outputs="text",
# examples=[
# [os.path.join(os.path.dirname("."),"audio/chalyaami.mp3")],
# [os.path.join(os.path.dirname("."),"audio/ekdonteen.flac")],
# [os.path.join(os.path.dirname("."),"audio/heyatachadjaale.mp3")],
# ],
# title="Whisper Konkani",
# description="Realtime demo for Konkani speech recognition using a fine-tuned Whisper small model.",
# )
# iface.launch() |