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#!/usr/bin/env python3
"""
Example usage of Wave2Vec2Inference with dynamic model switching
"""
from src.AI_Models.wave2vec_inference import (
create_inference,
get_available_models,
get_model_name,
DEFAULT_MODEL
)
def main():
print("=== Wave2Vec2 Model Selection Example ===\n")
# Show available models
print("Available models:")
models = get_available_models()
for key, model_name in models.items():
print(f" {key}: {model_name}")
print(f"\nDefault model: {DEFAULT_MODEL}\n")
# Example 1: Using default model
print("1. Creating inference with default model:")
asr_default = create_inference()
print(f" Loaded: {asr_default.model_name}\n")
# Example 2: Using model key
print("2. Creating inference with model key 'english_large':")
asr_key = create_inference("english_large")
print(f" Loaded: {asr_key.model_name}\n")
# Example 3: Using full model name
print("3. Creating inference with full model name:")
asr_full = create_inference("facebook/wav2vec2-base-960h")
print(f" Loaded: {asr_full.model_name}\n")
# Example 4: Dynamic model switching
print("4. Dynamic model switching:")
model_keys = ["english_large", "multilingual", "base_english"]
for model_key in model_keys:
print(f" Switching to: {model_key}")
asr = create_inference(model_key)
print(f" Active model: {asr.model_name}")
# Example transcription (if you have an audio file)
# result = asr.file_to_text("your_audio_file.wav")
# print(f" Result: {result}")
print()
# Example 5: Using with ONNX
print("5. Creating ONNX inference with model selection:")
try:
asr_onnx = create_inference("english_large", use_onnx=True)
print(f" ONNX model loaded: {asr_onnx.model_name}")
except Exception as e:
print(f" ONNX conversion needed: {e}")
print("\n=== Usage Examples ===")
print("# Use default model")
print("asr = create_inference()")
print()
print("# Use model key")
print("asr = create_inference('english_large')")
print()
print("# Use full model name")
print("asr = create_inference('facebook/wav2vec2-base-960h')")
print()
print("# Use with ONNX")
print("asr = create_inference('english_large', use_onnx=True)")
print()
print("# Transcribe audio")
print("result = asr.file_to_text('audio.wav')")
print("# or")
print("result = asr.buffer_to_text(audio_array)")
if __name__ == "__main__":
main()
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