Create app.py

app.py

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+import gradio as gr
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# Load your model from Hugging Face
+model_name = "Futuresony/Future-sw_ASR-24-02-2025"
+processor = Wav2Vec2Processor.from_pretrained(model_name)
+model = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+# Function to transcribe audio
+def transcribe(audio_file):
+    speech_array, sample_rate = torchaudio.load(audio_file)
+    
+    # Resample to 16kHz
+    resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
+    speech_array = resampler(speech_array).squeeze().numpy()
+    
+    # Process and transcribe
+    input_values = processor(speech_array, sampling_rate=16000, return_tensors="pt").input_values
+    with torch.no_grad():
+        logits = model(input_values).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    
+    # Decode the text
+    transcription = processor.batch_decode(predicted_ids)[0]
+    return transcription
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath"),
+    outputs="text",
+    title="Swahili ASR Transcription",
+    description="Upload a Swahili audio file, and the model will transcribe the speech.",
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()
+