Create app.py

app.py

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+
+import gradio as gr
+import soundfile as sf
+import torch
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+
+# Assuming 'transcribe' was defined in a previous cell.
+# If not, define it here or import it from the correct module.
+
+# Create a placeholder for ASR_LANGUAGES if it's not defined elsewhere.
+ASR_LANGUAGES = {"eng": "English", "swh": "Swahili"}  # Replace with your actual languages
+
+# ✅ Define or Re-define the `transcribe` function within this cell
+MODEL_ID = "facebook/mms-1b-all" # Make sure this is the same model ID used for training
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+
+def transcribe(audio_path, language):
+    """Transcribes an audio file using the fine-tuned model."""
+    # Set the target language based on user selection
+    if language:
+        target_lang = language.split(" ")[0]  # Extract language code
+        processor.tokenizer.set_target_lang(target_lang)
+        if target_lang != "eng":  # Load adapter if not English
+            model.load_adapter(target_lang)
+
+    audio, samplerate = sf.read(audio_path)
+    inputs = processor(audio, sampling_rate=samplerate, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs).logits
+
+    ids = torch.argmax(outputs, dim=-1)[0]
+    return processor.decode(ids)
+
+
+mms_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(),
+        gr.Dropdown(
+            [f"{k} ({v})" for k, v in ASR_LANGUAGES.items()],
+            label="Language",
+            value="eng English",
+        ),
+    ],
+    outputs="text",
+    title="Speech-to-Text Transcription",
+    description="Transcribe audio input into text.",
+    allow_flagging="never",
+)
+
+with gr.Blocks() as demo:
+    mms_transcribe.render()
+
+if __name__ == "__main__":
+    demo.queue()
+    demo.launch()