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ainergiz
/
medasr-mlx-int8

Automatic Speech Recognition
MLX
Safetensors
English
lasr_ctc
audio
medical
speech-recognition
asr
conformer
ctc
apple-silicon
on-device
healthcare
quantized
int8
Eval Results (legacy)
Model card Files
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Upload MedASR MLX int8 model

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Files changed (7) hide show
  1. README.md +152 -0
  2. config.json +318 -0
  3. processor/processor_config.json +15 -0
  4. processor/tokenizer.json +3082 -0
  5. processor/tokenizer_config.json +246 -0
  6. quantization_report.json +20 -0
  7. weights.npz +3 -0
README.md ADDED
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+ ---
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+ language:
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+ - en
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+ license: other
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+ license_name: hai-def-terms-of-use
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+ license_link: https://developers.google.com/health-ai-developer-foundations/terms
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+ library_name: mlx
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+ pipeline_tag: automatic-speech-recognition
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+ tags:
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+ - mlx
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+ - audio
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+ - medical
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+ - speech-recognition
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+ - asr
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+ - conformer
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+ - ctc
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+ - apple-silicon
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+ - on-device
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+ - healthcare
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+ - quantized
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+ - int8
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+ base_model: ainergiz/medasr-mlx-fp16
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+ base_model_relation: quantized
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+ model-index:
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+ - name: MedASR-MLX-INT8
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+ results:
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+ - task:
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+ type: automatic-speech-recognition
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+ metrics:
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+ - name: WER parity vs PyTorch
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+ type: wer
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+ value: 0.0
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+ - name: Token Agreement vs PyTorch
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+ type: accuracy
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+ value: 100.0
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+ ---
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+
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+ # MedASR-MLX (int8)
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+
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+ **8-bit quantized version of [MedASR-MLX](https://huggingface.co/ainergiz/medasr-mlx-fp16) — 40% smaller with zero quality loss.**
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+
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+ Google's MedASR 105M Conformer-CTC, converted to MLX and quantized to 8-bit affine (group size 64). Runs natively on Apple Silicon with **0.0% WER degradation** vs the original PyTorch model.
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+
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+ > **Full precision version:** [`ainergiz/medasr-mlx-fp16`](https://huggingface.co/ainergiz/medasr-mlx-fp16) (201 MB)
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+
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+ ## Model Details
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+
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+ | Property | Value |
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+ |----------|-------|
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+ | **Base model** | [google/medasr](https://huggingface.co/google/medasr) → [ainergiz/medasr-mlx-fp16](https://huggingface.co/ainergiz/medasr-mlx-fp16) |
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+ | **Architecture** | LASR Conformer-CTC (17 layers, 512 hidden, 8 heads) |
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+ | **Parameters** | 105M |
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+ | **Weights** | 121 MB (int8, affine, group_size=64) |
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+ | **Quantization** | 8-bit affine on Linear/Embedding layers; Conv layers remain fp16 |
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+ | **Vocab** | 512 tokens (SentencePiece) |
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+ | **Audio input** | 16 kHz mono, 128-bin mel spectrogram |
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+ | **Framework** | MLX (Apple Silicon native) |
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+
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+ ## Performance
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+
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+ Benchmarked on Apple M4 Pro (24 GB), 43.8-second medical audio clip:
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+
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+ | Metric | int8 (this model) | fp16 | HF PyTorch (fp32) |
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+ |--------|--------------------|------|-------------------|
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+ | Latency | **0.16s** | 0.09s | 0.9-1.6s |
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+ | Real-Time Factor | **0.004** | 0.002 | 0.02-0.04 |
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+ | Weights on disk | **121 MB** | 201 MB | ~421 MB |
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+ | WER vs PyTorch | **0.0%** | 0.0% | — |
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+ | Token Agreement | **100%** | 100% | — |
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+
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+ **Lossless quantization** — 40% smaller with identical output tokens.
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+
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+ ## Quantization Details
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+
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+ - **Method:** `mlx.nn.quantize` (affine mode)
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+ - **Bits:** 8
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+ - **Group size:** 64
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+ - **Target modules:** Linear and Embedding layers (MLX default predicate)
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+ - **Convolution layers:** Remain in float16 (quantizing Conv1d destroys accuracy for this architecture)
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+ - **Source:** Quantized from `artifacts/medasr-mlx-fp16`
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+
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+ ## Usage
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+
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+ ### Requirements
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+
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+ ```bash
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+ pip install mlx numpy soundfile
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+ ```
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+
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+ You also need the model code from the [MedASR-MLX repository](https://github.com/ainergiz/medasr-mlx):
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+
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+ ```bash
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+ git clone https://github.com/ainergiz/medasr-mlx.git
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+ cd medasr-mlx
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+ ```
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+
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+ ### Transcribe audio
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+
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+ ```python
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+ from model import MedASRModel
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+ from audio_utils import load_audio_mono
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+ import mlx.core as mx
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+
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+ # Load model (automatically applies int8 quantization from config)
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+ model_dir = "artifacts/medasr-mlx-int8" # or download from HF
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+ model = MedASRModel.from_pretrained(model_dir)
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+
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+ # Load audio and run inference (same API as fp16)
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+ audio = load_audio_mono("your_audio.wav", target_sr=16000)
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+ # ... (see transcribe_mlx.py for full pipeline)
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+ ```
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+
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+ ### Full pipeline
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+
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+ ```bash
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+ python main.py audio/eval-consultation/clip_0001.wav
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+ ```
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+
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+ ## Intended Use
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+
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+ MedASR is designed for medical speech recognition — doctor-patient conversations, clinical dictation, and medical terminology. This int8 variant is ideal when storage is constrained but you need lossless accuracy.
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+
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+ **This model is not intended for clinical diagnosis or treatment without appropriate validation and regulatory authorization.**
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+
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+ ## Limitations
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+
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+ - English only
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+ - Optimized for medical domain speech
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+ - Raw output includes formatting tokens (`{period}`, `{comma}`, etc.) requiring post-processing
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+ - Requires Apple Silicon hardware (M1+ Mac or A17+ iPhone)
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+
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+ ## License
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+
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+ The use of this model is governed by the [Health AI Developer Foundations Terms of Use](https://developers.google.com/health-ai-developer-foundations/terms). Source code components are licensed under Apache 2.0.
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+
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+ > HAI-DEF is provided under and subject to the Health AI Developer Foundations Terms of Use.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @misc{medasr-mlx,
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+ title={MedASR-MLX: On-Device Medical Speech Recognition for Apple Silicon},
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+ author={Ali Ihsan Nergiz},
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+ year={2026},
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+ url={https://huggingface.co/ainergiz/medasr-mlx-int8}
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+ }
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+ ```
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+
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+ ## Acknowledgments
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+
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+ - [Google Health AI Developer Foundations](https://developers.google.com/health-ai-developer-foundations) for the original MedASR model
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+ - [MLX team at Apple](https://github.com/ml-explore/mlx) for the framework
config.json ADDED
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+ {
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+ "vocab_size": 512,
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+ "ctc_loss_reduction": "mean",
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+ "ctc_zero_infinity": true,
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+ "encoder_config": {
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+ "rope_parameters": {
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+ "rope_theta": 10000.0,
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+ "rope_type": "default"
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+ },
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+ "layer_norm_eps": 1e-06,
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+ "feed_forward_residual_weights": [
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+ 1.5,
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+ "conv_residual_weights": [
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+ "subsampling_conv_stride": 2,
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+ "subsampling_conv_channels": 256,
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+ "num_mel_bins": 128,
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+ "dropout": 0.1,
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+ "dropout_positions": 0.0,
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+ "model_type": "lasr_encoder",
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+ "timestamp_utc": "2026-02-09T18:36:08.983149+00:00",
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+ "weights_dtype": "affine-int8",
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+ "converter": "convert.py",
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+ "weight_layout_notes": {
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+ "linear": "unchanged (out_features, in_features)",
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+ "conv1d": "transposed from torch [out, in, kernel] to mlx [out, kernel, in]",
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+ "*.num_batches_tracked"
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+ "max_seq_len_cached": 10000,
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+ "inv_freq_shape": [
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+ 32
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+ ]
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+ }
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+ },
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+ "_audio": {
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+ "feature_size": 128,
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+ "sampling_rate": 16000,
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+ "padding_value": 0.0,
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+ "padding_side": "right",
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+ "return_attention_mask": true,
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+ "_processor_class": "LasrProcessor",
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+ "feature_extractor_type": "LasrFeatureExtractor",
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+ "hop_length": 160,
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+ "n_fft": 512,
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+ "win_length": 400
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+ },
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+ "_quantization": {
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+ "enabled": true,
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+ "bits": 8,
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+ "group_size": 64,
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+ "mode": "affine",
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+ "target_modules": "mlx.nn.quantize default predicate (Linear/Embedding layers)",
311
+ "source_model_dir": "artifacts/medasr-mlx-fp16",
312
+ "timestamp_utc": "2026-02-09T20:38:09.851556+00:00",
313
+ "notes": [
314
+ "Convolution layers remain non-quantized with this path.",
315
+ "Model loader applies nn.quantize() before loading quantized weights."
316
+ ]
317
+ }
318
+ }
processor/processor_config.json ADDED
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1
+ {
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+ "feature_extractor": {
3
+ "feature_extractor_type": "LasrFeatureExtractor",
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+ "feature_size": 128,
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+ "hop_length": 160,
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+ "n_fft": 512,
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+ "padding_side": "right",
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+ "padding_value": 0.0,
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+ "processor_class": "LasrProcessor",
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+ "return_attention_mask": true,
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+ "sampling_rate": 16000,
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+ "win_length": 400
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+ },
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+ "processor_class": "LasrProcessor"
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+ }
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