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# perplexity-tablette |
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## Installation |
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L'environnement du dépôt est configuré avec `uv`. Pour l'installer: |
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```shell |
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uv sync |
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``` |
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## Usage |
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Pour lancer le script dans l'environnement (sans avoir à l'activer): |
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```shell |
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uv run main.py test_audio.ogg --model base |
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``` |
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NOTE: le modèle réellement utilisé pour les prédictions dans le papier était "large" |
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import argparse |
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import pickle |
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from pathlib import Path |
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import numpy as np |
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import whisper |
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from sklearn.pipeline import Pipeline |
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DEMOG_STATS = { |
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'DEMOG_AGE': { |
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'mean': 52.971585365853656, |
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'std': 10.597689763329704}, |
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'DEMOG_SEX': { |
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'mean': 1.5365853658536586, |
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'std': 0.5017284187715013}, |
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'HIST_CAG_NBALLEL2': { |
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'mean': 43.5609756097561, |
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'std': 2.867861770588018, }} |
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PREDICTED_SCORES = {'cUHDRS': 'cUHDRS', |
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'UHDRS_SCORE_MOTEUR': 'TMS', |
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'UHDRS_TFC_SCORE': 'TFC'} |
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def extract_logprob_feats(audio_path: Path): |
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model = whisper.load_model("base") |
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result = model.transcribe(str(audio_path)) |
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logprobs = [segment["avg_logprob"] for segment in result["segments"]] |
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mean_logprob = np.mean(logprobs) |
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std_logprob = np.std(logprobs) |
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return mean_logprob, std_logprob |
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def main(): |
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parser = argparse.ArgumentParser(description="Analyze audio transcription perplexity") |
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parser.add_argument("audio_file", type=str, help="Path to the audio file to analyze") |
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parser.add_argument("--model", type=str, default="large", |
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choices=["tiny", "base", "small", "medium", "large"], |
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help="Size of the whisper model to use") |
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args = parser.parse_args() |
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audio_path = Path(args.audio_file) |
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if not audio_path.exists(): |
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raise FileNotFoundError(f"Audio file not found: {audio_path}") |
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mean_logprob, std_logprob = extract_logprob_feats(audio_path) |
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print(f"Mean log probability: {mean_logprob:.4f}") |
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print(f"Standard deviation: {std_logprob:.4f}") |
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feats = { |
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'mean_logprob': mean_logprob, |
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'std_logprob': std_logprob, |
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} |
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# sample a random (fake) value for each demog feature |
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for demog_name, demog_stats in DEMOG_STATS.items(): |
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feats[demog_name] = np.random.normal(demog_stats['mean'], demog_stats['std']) |
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feat_names_order = ['mean_logprob', 'std_logprob', 'DEMOG_AGE', 'HIST_CAG_NBALLEL2', 'DEMOG_SEX'] |
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feat_array = np.array([feats[feat_name] for feat_name in feat_names_order]) |
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# for each score, loading the corresponding model and predicting the score |
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for score_real_name, score_name in PREDICTED_SCORES.items(): |
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with open(f"models/reg_lin_{score_real_name}.pkl", 'rb') as f: |
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score_model: Pipeline = pickle.load(f) |
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predicted_score = score_model.predict(feat_array.reshape(1, -1)) |
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print(f"Predicted {score_name}: {predicted_score}") |
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if __name__ == "__main__": |
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main() |
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[project] |
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name = "perplexity-tablette" |
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version = "0.1.0" |
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description = "Implémentation du modèle basé perplexité pour le NPI" |
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requires-python = ">=3.12" |
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dependencies = [ |
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"openai-whisper>=20250625", |
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"scikit-learn>=1.7.1", |
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] |
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version = 1 |
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revision = 3 |
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requires-python = ">=3.12" |
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[[package]] |
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Loading…
Reference in new issue