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import os
import torch
import gradio as gr
import mimetypes
from pydub import AudioSegment
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
# Set device and precision
device = "cpu"
torch_dtype = torch.float32
# Load KB-Whisper model
model_id = "KBLab/kb-whisper-large"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype
).to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
device=device,
torch_dtype=torch_dtype,
)
def transcribe(audio_path):
try:
# Get file extension
ext = os.path.splitext(audio_path)[1].lower()
# Convert to WAV if not already
if ext != ".wav":
try:
sound = AudioSegment.from_file(audio_path)
converted_path = audio_path.replace(ext, ".converted.wav")
sound.export(converted_path, format="wav")
audio_path = converted_path
except Exception as e:
return f"Error converting audio to WAV: {str(e)}"
# Transcribe
result = pipe(audio_path, chunk_length_s=30, generate_kwargs={"task": "transcribe", "language": "sv"})
return result["text"]
except Exception as e:
return f"Transcription failed: {str(e)}"
# Gradio UI
gr.Interface(
fn=transcribe,
inputs=gr.Audio(type="filepath", label="Upload Audio (.m4a, .mp3, .wav)"),
outputs=gr.Textbox(label="Swedish Transcript"),
title="Swedish Speech Transcriber with KB-Whisper",
description="Supports .m4a, .mp3, .wav files. Transcribes spoken Swedish using KBLab's Whisper Large model. May take time on CPU.",
).launch() |