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ASR_deployment.py

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+# -*- coding: utf-8 -*-
+"""ASR_Deployment.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1MmePYOn1Ho2FhILi00u9UbvsujEoHhot
+"""
+
+import gradio as gr
+from transformers import WhisperForConditionalGeneration, WhisperProcessor, GenerationConfig
+import torch
+import librosa
+import os
+
+# --- 1. CONFIGURATION ---
+# Note: Ensure your token has "Read" access to the repository
+MODEL_PATH = "MaryWambo/whisper-base-kikuyu4"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# --- 2. LOAD MODEL & PROCESSOR ---
+print(f"Loading model to {device}...")
+try:
+    processor = WhisperProcessor.from_pretrained(MODEL_PATH)
+    model = WhisperForConditionalGeneration.from_pretrained(MODEL_PATH).to(device)
+
+    # Define Generation Config to avoid "outdated" errors
+    # We set language and task here so they don't conflict in the generate() call
+    gen_config = GenerationConfig.from_pretrained(MODEL_PATH)
+    gen_config.language = "swahili" # Using full name or "sw" depending on how it was trained
+    gen_config.task = "transcribe"
+    gen_config.forced_decoder_ids = None
+    gen_config.suppress_tokens = []
+
+    model.generation_config = gen_config
+
+except Exception as e:
+    print(f"Error loading model: {e}")
+
+# --- 3. CUSTOM CSS ---
+custom_css = """
+body, .gradio-container { background-color: white !important; }
+#title-text h1 { color: #8b0000 !important; font-weight: 900 !important; text-align: center; }
+.upload-button svg, .mic-button svg, .clear-button svg, .record-button svg {
+    transform: scale(1.5) !important;
+    color: #8b0000 !important;
+}
+#predict-box textarea {
+    font-size: 1.6rem !important;
+    font-weight: 800 !important;
+    color: #000000 !important;
+    border: 3px solid #8b0000 !important;
+}
+#run-btn {
+    background: #8b0000 !important;
+    color: white !important;
+    font-weight: bold !important;
+    font-size: 1.4rem !important;
+}
+"""
+
+# --- 4. LOGIC FUNCTIONS ---
+def transcribe_kikuyu(audio):
+    if audio is None:
+        return "Please record or upload audio."
+
+    try:
+        # Load audio and resample to 16kHz (standard for Whisper)
+        speech_array, sampling_rate = librosa.load(audio, sr=16000)
+
+        # Process audio features
+        inputs = processor(speech_array, sampling_rate=sampling_rate, return_tensors="pt")
+        input_features = inputs.input_features.to(device)
+
+        with torch.no_grad():
+            # We no longer pass 'language' or 'task' here because
+            # they are already defined in model.generation_config
+            generated_ids = model.generate(
+                input_features=input_features,
+                num_beams=5,
+                max_new_tokens=255
+            )
+
+        # Decode the predicted IDs to text
+        prediction = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+        return prediction
+
+    except Exception as e:
+        return f"Error during transcription: {str(e)}"
+
+# --- 5. BUILD GRADIO UI ---
+with gr.Blocks(theme=gr.themes.Default(), css=custom_css) as demo:
+    gr.Markdown("# 🎙️ Kikuyu ASR ", elem_id="title-text")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            audio_input = gr.Audio(
+                sources=["microphone", "upload"],
+                type="filepath",
+                label="🎤 Record/Upload Kikuyu Speech"
+            )
+            submit_btn = gr.Button("🚀 RUN TRANSCRIPTION", elem_id="run-btn")
+
+        with gr.Column(scale=1):
+            text_out = gr.Textbox(
+                label="🤖 AI Prediction",
+                elem_id="predict-box",
+                lines=8
+            )
+
+    submit_btn.click(
+        fn=transcribe_kikuyu,
+        inputs=[audio_input],
+        outputs=[text_out]
+    )
+
+# --- 6. LAUNCH ---
+if __name__ == "__main__":
+    # share=True creates a public URL valid for 72 hours
+    demo.launch(share=True, debug=True)
+

requirements.txt

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+gradio
+transformers
+torch
+librosa
+datasets
+accelerate
+soundfile