app.py

import gradio as gr
from transformers import WhisperForConditionalGeneration
from transformers import WhisperFeatureExtractor
from transformers import WhisperTokenizer
from transformers import pipeline

checkpoint = "tsobolev/whisper-small-ka"

feature_extractor = WhisperFeatureExtractor.from_pretrained(checkpoint)
tokenizer = WhisperTokenizer.from_pretrained(checkpoint, language="georgian", task="transcribe")
model = WhisperForConditionalGeneration.from_pretrained(checkpoint)
forced_decoder_ids = tokenizer.get_decoder_prompt_ids(language="georgian", task="transcribe")

asr_pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    feature_extractor=feature_extractor,
    tokenizer=tokenizer,
    chunk_length_s=30,
    stride_length_s=(4, 2)
)

def transcribe_ge(speech):
    text = asr_pipe(
            speech,
            generate_kwargs={"forced_decoder_ids": forced_decoder_ids}
    )["text"]
    return text




title = "Whisper small finetuned on CV14 dataset"
description = """
Demo for speech-to-text translation
"""

demo = gr.Blocks()

mic_translate = gr.Interface(
    fn=transcribe_ge,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs=gr.Textbox(),
    title=title,
    description=description,
)

file_translate = gr.Interface(
    fn=transcribe_ge,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs=gr.Textbox(),
    examples=[["./example.wav"]],
    title=title,
    description=description,
)

with demo:
    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])

demo.launch()