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README.md

@@ -5,7 +5,6 @@ datasets:
 - voxceleb2
 libraries:
 - speechbrain
-- transformers
 tags:
 - gender-classification
 - speaker-characteristics
@@ -35,36 +34,34 @@ The model was trained on VoxCeleb2 dataset:
 - 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
-  - Applied SileroVAD for voice activity detection
+  - Converted to WAV format, single channel, 16kHz sampling rate, 256 kp/s bitrate
+  - Applied SileroVAD for voice activity detection, taking the first voiced segment
 
 ## Installation
-Install the required dependencies:
-```bash
-pip install -r requirements.txt
-```
-Or install individually:
+
+You can install the package directly from GitHub:
+
 ```bash
-pip install scikit-learn pandas soundfile speechbrain torch torchaudio transformers
+pip install git+https://github.com/griko/voice-gender-classification.git
 ```
 
 ## Usage
 
 ```python
-from transformers import pipeline
-from modeling_gender import GenderClassificationPipeline
+from voice_gender_classification import GenderClassificationPipeline
 
 # Load the pipeline
-classifier = pipeline(
-    "audio-classification",
-    model="griko/gender_cls_svm_ecapa_voxceleb",
-    pipeline_class=GenderClassificationPipeline
+classifier = GenderClassificationPipeline.from_pretrained(
+    "griko/gender_cls_svm_ecapa_voxceleb"
 )
 
+# Single file prediction
 result = classifier("path/to/audio.wav")
 print(result)  # ["female"] or ["male"]
+
+# Batch prediction
+results = classifier(["audio1.wav", "audio2.wav"])
+print(results)  # ["female", "male", "female"]
 ```
 
 ## Limitations