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README.md
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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license: apache-2.0
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base_model: Jacaranda-Health/ASR-STT
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tags:
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- speech-to-text
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- automatic-speech-recognition
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- quantized
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- 4bit
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language:
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- en
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- sw
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pipeline_tag: automatic-speech-recognition
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# ASR-STT 4BIT Quantized
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This is a 4bit quantized version of [Jacaranda-Health/ASR-STT](https://huggingface.co/Jacaranda-Health/ASR-STT).
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## Model Details
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- **Base Model**: Jacaranda-Health/ASR-STT
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- **Quantization**: 4bit
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- **Size Reduction**: 84.6% smaller than original
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- **Original Size**: 2913.89 MB
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- **Quantized Size**: 448.94 MB
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## Usage
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```python
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, BitsAndBytesConfig
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import torch
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import librosa
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# Load processor
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processor = AutoProcessor.from_pretrained("eolang/ASR-STT-4bit")
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# Configure quantization
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_use_double_quant=True
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)
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# Load quantized model
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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"eolang/ASR-STT-4bit",
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quantization_config=quantization_config,
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device_map="auto"
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)
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# Transcription function
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def transcribe(filepath):
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audio, sr = librosa.load(filepath, sr=16000)
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inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
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# Convert to half precision for quantized models
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if torch.cuda.is_available():
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inputs = {k: v.cuda().half() for k, v in inputs.items()}
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else:
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inputs = {k: v.half() for k, v in inputs.items()}
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with torch.no_grad():
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generated_ids = model.generate(inputs["input_features"])
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return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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# Example usage
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transcription = transcribe("path/to/audio.wav")
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print(transcription)
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```
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## Performance
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- Faster inference due to reduced precision
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- Lower memory usage
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- Maintained transcription quality
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## Requirements
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- transformers
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- torch
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- bitsandbytes
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- librosa
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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