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import base64 |
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import os |
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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 librosa |
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from datetime import datetime |
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from datasets import ( |
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load_dataset, |
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concatenate_datasets, |
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Dataset, |
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DatasetDict, |
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Features, |
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Value, |
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Audio, |
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) |
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HF_DATASET_NAME = "BounharAbdelaziz/Moroccan-STT-Eval-Dataset" |
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MODEL_PATHS = { |
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"NANO": "BounharAbdelaziz/Morocco-Darija-STT-tiny", |
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"SMALL": "BounharAbdelaziz/Morocco-Darija-STT-small", |
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"LARGE": "BounharAbdelaziz/Morocco-Darija-STT-large-v1.2", |
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} |
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STT_MODEL_TOKEN = os.environ.get("STT_MODEL_TOKEN") |
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STT_EVAL_DATASET_TOKEN = os.environ.get("STT_EVAL_DATASET_TOKEN") |
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def encode_image_to_base64(image_path): |
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with open(image_path, "rb") as image_file: |
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encoded_string = base64.b64encode(image_file.read()).decode() |
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return encoded_string |
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def create_html_image(image_path): |
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img_base64 = encode_image_to_base64(image_path) |
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html_string = f""" |
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<div style="display: flex; justify-content: center; align-items: center; width: 100%; text-align: center;"> |
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<div style="max-width: 800px; margin: auto;"> |
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<img src="data:image/jpeg;base64,{img_base64}" |
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style="max-width: 75%; height: auto; display: block; margin: 0 auto; margin-top: 50px;" |
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alt="Displayed Image"> |
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</div> |
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</div> |
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""" |
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return html_string |
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def save_to_hf_dataset(audio_signal, model_choice, transcription): |
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print("[INFO] Loading dataset...") |
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dataset = load_dataset(HF_DATASET_NAME, token=STT_EVAL_DATASET_TOKEN) |
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print("[INFO] Dataset loaded successfully.") |
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") |
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new_entry = { |
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"audio": [{"array": audio_signal, "sampling_rate": 16000}], |
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"transcription": [transcription], |
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"model_used": [model_choice], |
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"timestamp": [timestamp], |
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} |
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new_dataset = Dataset.from_dict( |
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new_entry, |
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features=Features({ |
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"audio": Audio(sampling_rate=16000), |
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"transcription": Value("string"), |
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"model_used": Value("string"), |
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"timestamp": Value("string"), |
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}) |
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) |
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print("[INFO] Adding the new entry to the dataset...") |
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train_dataset = dataset["train"] |
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updated_train_dataset = concatenate_datasets([train_dataset, new_dataset]) |
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dataset["train"] = updated_train_dataset |
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print("[INFO] Pushing the updated dataset...") |
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dataset.push_to_hub(HF_DATASET_NAME, token=STT_EVAL_DATASET_TOKEN) |
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print("[INFO] Dataset updated and pushed successfully.") |
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def load_model(model_name): |
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model_id = MODEL_PATHS[model_name.upper()] |
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return pipeline("automatic-speech-recognition", model=model_id, token=STT_MODEL_TOKEN) |
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def process_audio(audio, model_choice, save_data): |
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pipe = load_model(model_choice) |
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audio_signal = audio[1] |
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sample_rate = audio[0] |
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audio_signal = audio_signal.astype(np.float32) |
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if np.abs(audio_signal).max() > 1.0: |
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audio_signal = audio_signal / 32768.0 |
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if sample_rate != 16000: |
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print(f"[INFO] Resampling audio from {sample_rate}Hz to 16000Hz") |
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audio_signal = librosa.resample( |
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y=audio_signal, |
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orig_sr=sample_rate, |
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target_sr=16000 |
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) |
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result = pipe(audio_signal) |
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transcription = result["text"] |
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if save_data: |
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print(f"[INFO] Saving data to eval dataset...") |
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save_to_hf_dataset(audio_signal, model_choice, transcription) |
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return transcription |
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def create_interface(): |
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with gr.Blocks(css="footer{display:none !important}") as app: |
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base_path = os.path.dirname(__file__) |
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local_image_path = os.path.join(base_path, 'logo_image.png') |
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gr.HTML(create_html_image(local_image_path)) |
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gr.Markdown("# 🇲🇦 🚀 Moroccan Fast Speech-to-Text Transcription 😍") |
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gr.Markdown("⚠️ **Nota bene**: Make sure to click on **Stop** before hitting the **Transcribe** button") |
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gr.Markdown("📌 The **Large** model should be available soon. Stay tuned!") |
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with gr.Row(): |
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model_choice = gr.Dropdown( |
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choices=["Nano", "Small", "Large"], |
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value="Small", |
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label="Select one of the models" |
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) |
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with gr.Row(): |
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audio_input = gr.Audio( |
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sources=["microphone"], |
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type="numpy", |
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label="Record Audio", |
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) |
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with gr.Row(): |
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save_data = gr.Checkbox( |
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label="Contribute to the evaluation benchmark", |
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value=True |
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) |
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submit_btn = gr.Button("Transcribe 🔥") |
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output_text = gr.Textbox(label="Transcription") |
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gr.Markdown(""" |
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### 📄📌 Notice to our dearest users 🤗 |
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- By transcribing your audio, you’re actively contributing to the development of a benchmark evaluation dataset for Moroccan speech-to-text models. |
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- Your transcriptions will be logged into a dedicated Hugging Face dataset, playing a crucial role in advancing research and innovation in speech recognition for Moroccan dialects and languages. |
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- Together, we’re building tools that better understand and serve the unique linguistic landscape of Morocco. |
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- We count on your **thoughtfulness and responsibility** when using the app. Thank you for your contribution! 🌟 |
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""") |
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submit_btn.click( |
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fn=process_audio, |
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inputs=[audio_input, model_choice, save_data], |
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outputs=output_text |
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) |
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gr.Markdown("<br/>") |
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return app |
