app.py

from transformers import pipeline
import gradio as gr
from pydub import AudioSegment
import os

# Initialize the pipeline

pipe = pipeline(model="srirama/whisper-small-hi")  # change to "your-username/the-name-you-picked"



# Function to split audio into chunks
def split_audio(audio_path, chunk_length_ms):
    audio = AudioSegment.from_file(audio_path)
    chunks = []
    for i in range(0, len(audio), chunk_length_ms):
        chunk = audio[i:i + chunk_length_ms]
        chunks.append(chunk)
    return chunks

# Function to transcribe audio
def transcribe(audio_path):
    chunk_length_ms = 20 * 1000  # 20 seconds in milliseconds
    audio_chunks = split_audio(audio_path, chunk_length_ms)
    full_transcript = ""

    for i, chunk in enumerate(audio_chunks):
        temp_file = f"temp_chunk_{i}.wav"
        chunk.export(temp_file, format="wav")

        text = pipe(temp_file)["text"]
        full_transcript += text + " "

        os.remove(temp_file)  # Clean up the temporary file

    return full_transcript.strip()

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources=["microphone"], type="filepath"),
    outputs="text",
    title="Whisper Small English",
    description="Realtime demo for Dental speech recognition using a fine-tuned Whisper small model.",
)
# Launch the interface
iface.launch()