app.py

import gradio as gr
import warnings
import torch
from transformers import WhisperTokenizer, WhisperForConditionalGeneration, WhisperProcessor
import soundfile as sf



warnings.filterwarnings("ignore")

# Load tokenizer + model
tokenizer = WhisperTokenizer.from_pretrained("NbAiLabBeta/nb-whisper-medium")
model = WhisperForConditionalGeneration.from_pretrained("NbAiLabBeta/nb-whisper-medium")
processor = WhisperProcessor.from_pretrained("NbAiLabBeta/nb-whisper-medium")

# set up device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
torch_dtype = torch.float32

# move model to device
model.to(device)


def transcribe_audio(audio_file):
    audio_input, sample_rate = sf.read(audio_file)
    chunk_size = 16000 * 28  # 28 seconds chunks, seems to work best
    chunks = [audio_input[i:i + chunk_size] for i in range(0, len(audio_input), chunk_size)]

    transcription = ""
    for chunk in chunks:
        inputs = processor(chunk, sampling_rate=16000, return_tensors="pt")
        inputs = inputs.to(device)
        with torch.no_grad():
            output = model.generate(
                inputs.input_features,
                max_length=1024,  # Increase max_length@longer outputs
                num_beams=5,
                task="transcribe",
                language="no"
            )
        transcription += processor.batch_decode(output, skip_special_tokens=True)[0] + " "

    return transcription.strip()

# HTML |banner image
banner_html = """
<div style="text-align: center;">
    <img src="https://huggingface.co/spaces/camparchimedes/ola_s-audioshop/raw/main/Olas%20AudioSwitch%20Shop.png" alt="Banner" width="87%; height:auto;">
</div>
"""

# Gradio interface
iface = gr.Blocks()

with iface:
    gr.HTML(banner_html)
    gr.Markdown("# Nvidia A100👋🏼👾🦾⚡☕🧑🏼‍🏫@{NbAiLab/whisper-norwegian-medium}\nUpload audio file (*needs to be in .mp3 format before upload*)")
    audio_input = gr.Audio(type="filepath")
    transcription_output = gr.Textbox()
    transcribe_button = gr.Button("Transcribe")

    transcribe_button.click(fn=transcribe_audio, inputs=audio_input, outputs=transcription_output)

# Launch interface
iface.launch(share=True, debug=True)