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@@ -27,7 +27,7 @@ This model combines the SpeechBrain ECAPA-TDNN speaker embedding model with an S
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  - TIMIT test set: 6.02 cm Mean Absolute Error (MAE)
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  ## Training Data
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- The model was trained on VoxCeleb2 dataset:
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  - Audio preprocessing:
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  - Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate
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  - Applied SileroVAD for voice activity detection, taking the first voiced segment
@@ -43,7 +43,7 @@ pip install git+https://github.com/griko/voice-height-regression.git
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  ## Usage
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  ```python
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- from height_regressor import HeightRegressionPipeline
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  # Load the pipeline
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  regressor = HeightRegressionPipeline.from_pretrained(
 
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  - TIMIT test set: 6.02 cm Mean Absolute Error (MAE)
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  ## Training Data
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+ The model was trained on height enriched VoxCeleb2 dataset (for details read the paper):
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  - Audio preprocessing:
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  - Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate
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  - Applied SileroVAD for voice activity detection, taking the first voiced segment
 
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  ## Usage
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  ```python
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+ from voice_height_regressor import HeightRegressionPipeline
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  # Load the pipeline
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  regressor = HeightRegressionPipeline.from_pretrained(