| |
| import os |
| import gradio as gr |
| import spaces |
| from transformers import pipeline |
|
|
| MODEL_NAME = "palli23/whisper-small-sam_spjall" |
|
|
| @spaces.GPU(duration=60) |
| def transcribe_3min(audio_path): |
| if not audio_path: |
| return "Hladdu upp hljóðskrá" |
| |
| |
| pipe = pipeline( |
| "automatic-speech-recognition", |
| model=MODEL_NAME, |
| device=0, |
| token=os.getenv("HF_TOKEN") |
| ) |
| |
| result = pipe( |
| audio_path, |
| chunk_length_s=30, |
| stride_length_s=(6, 0), |
| return_timestamps=False, |
| batch_size=8 |
| ) |
| |
| return result["text"] |
|
|
| |
| with gr.Blocks(title="Íslenskt ASR – 3 mín") as demo: |
| gr.Markdown("# Íslenskt ASR – 3 mínútur") |
| gr.Markdown("**Whisper · Very low WER · 0.5-5minute audio transcribe á ZeroGPU**") |
| |
| audio = gr.Audio(type="filepath", label="Hladdu upp .mp3 / .wav (allt að 3 mín)") |
| btn = gr.Button("Transcribe", variant="primary", size="lg") |
| out = gr.Textbox(lines=30, label="Útskrift") |
| |
| btn.click(transcribe_3min, inputs=audio, outputs=out) |
|
|
| demo.launch(auth=("beta", "beta2025")) |