app.py

import gradio as gr
import torch
import torchaudio
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline

model_id = "lyhourt/whisper-small-clean_6-v4"

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=30,  # You can increase this if needed
    batch_size=16,
    return_timestamps=True,
    torch_dtype=torch_dtype,
    device=device,
)

def transcribe(audio_path):
    waveform, sample_rate = torchaudio.load(audio_path)
    # Split the audio into chunks of 30 seconds (or your desired chunk length)
    chunk_length = 30 * sample_rate  # 30 seconds
    chunks = [waveform[:, i:i + chunk_length] for i in range(0, waveform.size(1), chunk_length)]

    texts = []
    for chunk in chunks:
        chunk = chunk.to(device)
        text = pipe(chunk)["text"]
        texts.append(text)
    
    # Concatenate all texts
    full_text = " ".join(texts)
    return full_text

iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(sources=["upload"], type="filepath"),
    outputs="text",
    title="Whisper Small Hungarian",
    description="Realtime demo for Hungarian speech recognition using a fine-tuned Whisper small.",
)

iface.launch()