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import gradio as gr |
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from transformers import pipeline |
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import numpy as np |
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import os |
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from huggingface_hub import login |
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import librosa |
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import spaces |
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HF_TOKEN = os.environ.get("HF_TOKEN") |
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if HF_TOKEN: |
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login(token=HF_TOKEN) |
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MODEL_ID = "badrex/w2v-bert-2.0-kinyarwanda-asr" |
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transcriber = pipeline("automatic-speech-recognition", model=MODEL_ID) |
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@spaces.GPU |
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def transcribe(audio): |
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sr, y = audio |
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if y.ndim > 1: |
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y = y.mean(axis=1) |
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y = y.astype(np.float32) |
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y /= np.max(np.abs(y)) |
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return transcriber({"sampling_rate": sr, "raw": y})["text"] |
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examples = [] |
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examples_dir = "examples" |
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if os.path.exists(examples_dir): |
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for filename in os.listdir(examples_dir): |
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if filename.endswith((".wav", ".mp3", ".ogg")): |
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examples.append([os.path.join(examples_dir, filename)]) |
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print(f"Found {len(examples)} example files") |
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else: |
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print("Examples directory not found") |
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demo = gr.Interface( |
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fn=transcribe, |
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inputs=gr.Audio(), |
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outputs="text", |
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title="<div>ASRwanda 🎙️ <br>Speech Recognition for Kinyarwanda</div>", |
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description=""" |
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<div class="centered-content"> |
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<div> |
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<p> |
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Developed with ❤ by <a href="https://badrex.github.io/" style="color: #2563eb;">Badr al-Absi</a> ☕ |
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</p> |
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<br> |
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<p style="font-size: 15px; line-height: 1.8;"> |
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Muraho 👋🏼 |
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<br> |
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<br> |
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This is a demo for ASRwanda, a Transformer-based automatic speech recognition (ASR) system for Kinyarwanda language. |
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The underlying ASR model was trained on 500 hours of transcribed speech provided by |
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<a href="https://digitalumuganda.com/" style="color: #2563eb;">Digital Umuganda</a> as part of the Kinyarwanda |
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<a href="https://www.kaggle.com/competitions/kinyarwanda-automatic-speech-recognition-track-a" style="color: #2563eb;"> ASR hackathon</a> on Kaggle. |
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<br> |
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<p style="font-size: 15px; line-height: 1.8;"> |
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Simply <strong>upload an audio file</strong> 📤 or <strong>record yourself speaking</strong> 🎙️⏺️ to try out the model! |
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</p> |
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</div> |
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</div> |
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""", |
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examples=examples if examples else None, |
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cache_examples=False, |
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flagging_mode=None, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
