app.py

import torch
import gradio as gr
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
from urllib.parse import urlparse, parse_qs

import tempfile
import time
import os
import numpy as np

# Constants
MODEL_NAME = "dataprizma/whisper-large-v3-turbo"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000
YT_LENGTH_LIMIT_S = 3600  # 1 hour limit

# Device selection
device = 0 if torch.cuda.is_available() else "cpu"

# Load Whisper pipeline
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=9,
    device=device,
    model_kwargs={
#        "torch_dtype": torch.float16,
        "attn_implementation": "eager"
    },
)

# Transcription function (Fix applied)
def transcribe(audio_file, task):
    if audio_file is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting.")

    # Open file as binary to ensure correct data type
    with open(audio_file, "rb") as f:
        audio_data = f.read()

    # Read audio using ffmpeg_read (correcting input format)
    audio_array = ffmpeg_read(audio_data, pipe.feature_extractor.sampling_rate)
    
    duration = len(audio_array) / pipe.feature_extractor.sampling_rate
    print(f"Audio duration: {duration:.2f} seconds")

    # Convert to proper format
    inputs = {
        "array": np.array(audio_array),  
        "sampling_rate": pipe.feature_extractor.sampling_rate
    }
    
    generate_kwargs = {
        "task": task,
        "no_speech_threshold": 0.3,
        "logprob_threshold": -1.0,
        "compression_ratio_threshold": 2.4
    }
    
    # Perform transcription
    result = pipe(
        inputs,
        batch_size=BATCH_SIZE,
        generate_kwargs=generate_kwargs,
        return_timestamps=False
    )

    return result["text"]

# Gradio UI
demo = gr.Blocks()

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(type="filepath", label="Audio file"),
        gr.Radio(["transcribe", "translate"], label="Task"),
    ],
    outputs="text",
    title="Whisper Large V3: Transcribe Audio",
    description="Whisper Large V3 fine-tuned for Uzbek language by Dataprizma",
    flagging_mode="never",
)

with demo:
    gr.TabbedInterface([file_transcribe], ["Audio file"])

demo.launch()