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README.md

@@ -6,33 +6,28 @@ datasets:
 libraries:
 - speechbrain
 tags:
-- gender-classification
+- height-estimation
 - speaker-characteristics
 - speaker-recognition
 - audio-classification
 - voice-analysis
 ---
 
-# Gender Classification Model
+# Height Estimation Model
 
-This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVM classifier to predict speaker gender from audio input. The model was trained and evaluated on the VoxCeleb2, Mozilla Common Voice v10.0, and TIMIT datasets
+This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an SVR regressor to predict speaker height from audio input. The model was trained on the VoxCeleb2 and evaluated on the VoxCeleb2 and TIMIT datasets.
 
 ## Model Details
 - Input: Audio file (will be converted to 16kHz, mono, single channel)
-- Output: Gender prediction ("male" or "female")
+- Output: Predicted height in centimeters (continuous value)
 - Speaker embedding: 192-dimensional ECAPA-TDNN embedding from SpeechBrain
-- Classifier: Support Vector Machine optimized through Optuna (200 trials)
+- Regressor: Support Vector Regression optimized through Optuna
 - Performance: 
-  - VoxCeleb2 test set: 98.9% accuracy, 0.9885 F1-score
-  - Mozilla Common Voice v10.0 English validated test set: 92.3% accuracy
-  - TIMIT test set: 99.6% accuracy
+  - VoxCeleb2 test set: 6.01 cm Mean Absolute Error (MAE)
+  - TIMIT test set: 6.02 cm Mean Absolute Error (MAE)
 
 ## Training Data
 The model was trained on VoxCeleb2 dataset:
-- Training set: 1,691 speakers (845 females, 846 males)
-- Validation set: 785 speakers (396 females, 389 males)
-- Test set: 1,647 speakers (828 females, 819 males)
-- No speaker overlap between sets
 - Audio preprocessing: 
   - Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate
   - Applied SileroVAD for voice activity detection, taking the first voiced segment
@@ -42,35 +37,35 @@ The model was trained on VoxCeleb2 dataset:
 You can install the package directly from GitHub:
 
 ```bash
-pip install git+https://github.com/griko/voice-gender-classification.git
+pip install git+https://github.com/griko/voice-height-regression.git
 ```
 
 ## Usage
 
 ```python
-from voice_gender_classification import GenderClassificationPipeline
+from height_regressor import HeightRegressionPipeline
 
 # Load the pipeline
-classifier = GenderClassificationPipeline.from_pretrained(
-    "griko/gender_cls_svm_ecapa_voxceleb"
+regressor = HeightRegressionPipeline.from_pretrained(
+    "griko/height_reg_svr_ecapa_voxceleb"
 )
 
 # Single file prediction
-result = classifier("path/to/audio.wav")
-print(result)  # ["female"] or ["male"]
+result = regressor("path/to/audio.wav")
+print(f"Predicted height: {result[0]:.1f} cm")
 
 # Batch prediction
-results = classifier(["audio1.wav", "audio2.wav"])
-print(results)  # ["female", "male", "female"]
+results = regressor(["audio1.wav", "audio2.wav"])
+print(f"Predicted heights: {[f'{h:.1f}' for h in results]} cm")
 ```
 
 ## Limitations
 - Model was trained on celebrity voices from YouTube interviews
 - Performance may vary on different audio qualities or recording conditions
-- Designed for binary gender classification only
+- Height predictions are estimates and should not be used for medical or legal purposes
 
 ## Citation
 If you use this model in your research, please cite:
 ```bibtex
 TBD
-```
+```

config.json

@@ -1 +1 @@
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scaler.joblib

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-size 25165
+oid sha256:f2d7b9497213c91acc99e733a887de9a15959b9b7af412638f85ac9e57c11337
+size 11559

svr_model.joblib

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