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Update README.md

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@@ -162,7 +162,7 @@ device = "cuda:0" if torch.cuda.is_available() else "cpu"
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  torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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  # Specify the pre-trained model ID
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- model_id = "oriserve/Whisper-Hindi2Hinglish-Swift"
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  # Load the speech-to-text model with specified configurations
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  model = AutoModelForSpeechSeq2Seq.from_pretrained(
@@ -257,7 +257,7 @@ def save_model(model, save_path):
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  torch.save(pytorch_model, save_path)
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  # Load Hugging Face model
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- model_id = "oriserve/Whisper-Hindi2Hinglish-Swift"
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  model = AutoModelForSpeechSeq2Seq.from_pretrained(
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  model_id,
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  low_cpu_mem_usage=True, # Optimize memory usage
 
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  torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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  # Specify the pre-trained model ID
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+ model_id = "Oriserve/Whisper-Hindi2Hinglish-Swift"
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  # Load the speech-to-text model with specified configurations
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  model = AutoModelForSpeechSeq2Seq.from_pretrained(
 
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  torch.save(pytorch_model, save_path)
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  # Load Hugging Face model
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+ model_id = "Oriserve/Whisper-Hindi2Hinglish-Swift"
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  model = AutoModelForSpeechSeq2Seq.from_pretrained(
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  model_id,
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  low_cpu_mem_usage=True, # Optimize memory usage