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app.py

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+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")

requirements.txt

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+transformers>=4.38.0
+torch>=2.1.0
+torchaudio>=2.1.0
+gradio>=4.0.0