Delete README (1).md

README (1).md

@@ -1,80 +0,0 @@
----
-license: apache-2.0
-base_model: Jacaranda-Health/ASR-STT
-tags:
-- speech-to-text
-- automatic-speech-recognition
-- quantized
-- 8bit
-language:
-- en
-pipeline_tag: automatic-speech-recognition
----
-
-# ASR-STT 8BIT Quantized
-
-This is a 8bit quantized version of [Jacaranda-Health/ASR-STT](https://huggingface.co/Jacaranda-Health/ASR-STT).
-
-## Model Details
-- **Base Model**: Jacaranda-Health/ASR-STT
-- **Quantization**: 8bit
-- **Size Reduction**: 73.1% smaller than original
-- **Original Size**: 2913.89 MB
-- **Quantized Size**: 784.94 MB
-
-## Usage
-
-```python
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, BitsAndBytesConfig
-import torch
-import librosa
-
-# Load processor
-processor = AutoProcessor.from_pretrained("eolang/ASR-STT-8bit")
-
-# Configure quantization
-quantization_config = BitsAndBytesConfig(
-    load_in_8bit=True
-    llm_int8_threshold=6.0,
-    llm_int8_has_fp16_weight=False
-    
-)
-
-# Load quantized model
-model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    "eolang/ASR-STT-8bit",
-    quantization_config=quantization_config,
-    device_map="auto"
-)
-
-# Transcription function
-def transcribe(filepath):
-    audio, sr = librosa.load(filepath, sr=16000)
-    inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
-    
-    # Convert to half precision for quantized models
-    if torch.cuda.is_available():
-        inputs = {k: v.cuda().half() for k, v in inputs.items()}
-    else:
-        inputs = {k: v.half() for k, v in inputs.items()}
-    
-    with torch.no_grad():
-        generated_ids = model.generate(inputs["input_features"])
-    
-    return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-# Example usage
-transcription = transcribe("path/to/audio.wav")
-print(transcription)
-```
-
-## Performance
-- Faster inference due to reduced precision
-- Lower memory usage
-- Maintained transcription quality
-
-## Requirements
-- transformers
-- torch
-- bitsandbytes
-- librosa