| import torch | |
| import torchaudio | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| import gradio as gr | |
| model = Wav2Vec2ForCTC.from_pretrained("tacab/tacab_asr_somali") | |
| processor = Wav2Vec2Processor.from_pretrained("tacab/tacab_asr_somali") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| def transcribe(audio_path): | |
| waveform, sample_rate = torchaudio.load(audio_path) | |
| if sample_rate != 16000: | |
| waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform) | |
| if waveform.shape[0] > 1: | |
| waveform = waveform.mean(dim=0, keepdim=True) | |
| inputs = processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt") | |
| input_values = inputs.input_values.to(device) | |
| with torch.no_grad(): | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| transcription = processor.batch_decode(predicted_ids)[0] | |
| return transcription.lower() | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=gr.Audio(type="filepath", label="🎙️ Somali Audio"), | |
| outputs=gr.Text(label="📄 Transcription"), | |
| title="Tacab Somali ASR", | |
| description="Speak Somali and get transcription back!", | |
| ) | |
| iface.launch(server_name="0.0.0.0") |