Upload folder using huggingface_hub

b66d404 verified over 1 year ago

2.11 kB

#!/usr/bin/env python3
# Example usage for videoloc/seamless-translation

from transformers import AutoModel, AutoConfig
from huggingface_hub import hf_hub_download
import torch
import numpy as np
import importlib.util

def load_model_and_collator():
    # Load model - custom architecture requires importing the model class
    model_files = hf_hub_download(repo_id="videoloc/seamless-translation", filename="modeling_seamless_translation.py")
    spec = importlib.util.spec_from_file_location("modeling_seamless_translation", model_files)
    modeling_module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(modeling_module)

    # Now load the model using the custom class
    config = modeling_module.SeamlessTranslationConfig.from_pretrained("videoloc/seamless-translation")
    model = modeling_module.HFSeamlessTranslation.from_pretrained("videoloc/seamless-translation")
    
    # Load data collator
    collator_file = hf_hub_download(repo_id="videoloc/seamless-translation", filename="data_collator.py")
    spec = importlib.util.spec_from_file_location("data_collator", collator_file)
    collator_module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(collator_module)
    
    data_collator = collator_module.DataCollatorSimpleSeamless(
        processor="facebook/hf-seamless-m4t-medium",
        max_audio_length_sec=8.0,
        max_text_length=256
    )
    
    return model, data_collator

def example_inference():
    model, collator = load_model_and_collator()
    
    # Example data with translation awareness
    data = [{
        'raw_audio': np.random.randn(16000 * 3),  # 3 seconds at 16kHz
        'raw_text': "Example subtitle text for TTE prediction",
        'is_translation': 1,  # 1 for translated content, 0 for original
    }]
    
    batch = collator(data)
    model.eval()
    with torch.no_grad():
        outputs = model(**batch)
        tte_prediction = outputs.logits.item()
    
    print(f"Predicted Time To Edit (TTE): {tte_prediction:.2f} seconds")
    return tte_prediction

if __name__ == "__main__":
    example_inference()

	#!/usr/bin/env python3
	# Example usage for videoloc/seamless-translation

	from transformers import AutoModel, AutoConfig
	from huggingface_hub import hf_hub_download
	import torch
	import numpy as np
	import importlib.util

	def load_model_and_collator():
	# Load model - custom architecture requires importing the model class
	model_files = hf_hub_download(repo_id="videoloc/seamless-translation", filename="modeling_seamless_translation.py")
	spec = importlib.util.spec_from_file_location("modeling_seamless_translation", model_files)
	modeling_module = importlib.util.module_from_spec(spec)
	spec.loader.exec_module(modeling_module)

	# Now load the model using the custom class
	config = modeling_module.SeamlessTranslationConfig.from_pretrained("videoloc/seamless-translation")
	model = modeling_module.HFSeamlessTranslation.from_pretrained("videoloc/seamless-translation")

	# Load data collator
	collator_file = hf_hub_download(repo_id="videoloc/seamless-translation", filename="data_collator.py")
	spec = importlib.util.spec_from_file_location("data_collator", collator_file)
	collator_module = importlib.util.module_from_spec(spec)
	spec.loader.exec_module(collator_module)

	data_collator = collator_module.DataCollatorSimpleSeamless(
	processor="facebook/hf-seamless-m4t-medium",
	max_audio_length_sec=8.0,
	max_text_length=256
	)

	return model, data_collator

	def example_inference():
	model, collator = load_model_and_collator()

	# Example data with translation awareness
	data = [{
	'raw_audio': np.random.randn(16000 * 3), # 3 seconds at 16kHz
	'raw_text': "Example subtitle text for TTE prediction",
	'is_translation': 1, # 1 for translated content, 0 for original
	}]

	batch = collator(data)
	model.eval()
	with torch.no_grad():
	outputs = model(**batch)
	tte_prediction = outputs.logits.item()

	print(f"Predicted Time To Edit (TTE): {tte_prediction:.2f} seconds")
	return tte_prediction

	if __name__ == "__main__":
	example_inference()