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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() |