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README.md

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+---
+license: mit
+base_model: ai-sage/GigaAM-v3
+base_model_relation: quantized
+library_name: mlx
+pipeline_tag: automatic-speech-recognition
+language:
+  - ru
+tags:
+  - automatic-speech-recognition
+  - speech-recognition
+  - russian
+  - offline
+  - mlx
+  - apple
+  - apple-silicon
+  - ios
+  - ipados
+  - macos
+  - rnnt
+  - conformer
+  - sentencepiece
+  - native-apple
+---
+
+# GigaAM v3 MLX
+
+**GigaAM v3 MLX** is a native Apple MLX runtime bundle for offline Russian automatic speech recognition on iPhone, iPad, and Mac.
+
+This repository contains converted model assets for running `ai-sage/GigaAM-v3` revision `e2e_rnnt` with a native Apple runtime.
+
+The bundle is intended for native on-device inference on Apple platforms without Python, PyTorch, Transformers, torchaudio, librosa, pyannote, or server-side inference at runtime.
+
+## Model
+
+* Base model: `ai-sage/GigaAM-v3`
+* Revision: `e2e_rnnt`
+* Architecture: Conformer RNN-T
+* Language: Russian
+* Runtime target: native Apple MLX
+* Precision: FP16
+* Sample rate: 16 kHz
+* Audio channels: mono
+* Tokenizer: SentencePiece
+* Vocabulary size: 1024
+* Blank ID: 1024
+* Output classes: 1025
+
+## Target platforms
+
+This model bundle is intended for native Apple applications:
+
+| Platform | Target            |
+| -------- | ----------------- |
+| iOS      | iPhone            |
+| iPadOS   | iPad              |
+| macOS    | Apple Silicon Mac |
+
+## Intended use
+
+This bundle is intended for offline speech recognition in native Apple applications:
+
+* iPhone apps
+* iPad apps
+* macOS apps
+* local transcription tools
+* privacy-preserving offline ASR
+* Russian speech-to-text without cloud inference
+* native Swift/MLX ASR runtimes
+
+This repository contains model assets only. It is not a Python inference package.
+
+## Repository files
+
+```text
+README.md
+.gitattributes
+manifest.json
+checksums.sha256
+weights.fp16.safetensors
+tokenizer.model
+hann_window.f32.bin
+mel_filterbank_mel_freq.f32.bin
+```
+
+| File                              | Description                                                  |
+| --------------------------------- | ------------------------------------------------------------ |
+| `weights.fp16.safetensors`        | FP16 converted model weights                                 |
+| `tokenizer.model`                 | SentencePiece tokenizer                                      |
+| `manifest.json`                   | Runtime manifest with model, frontend, and decoding metadata |
+| `hann_window.f32.bin`             | Hann window used by the frontend                             |
+| `mel_filterbank_mel_freq.f32.bin` | Mel filterbank used by the frontend                          |
+| `checksums.sha256`                | SHA-256 checksums for integrity checks                       |
+| `.gitattributes`                  | Git LFS rules for binary model assets                        |
+
+## Runtime pipeline
+
+The intended native runtime pipeline is:
+
+```text
+Audio file / PCM samples
+→ native audio loader
+→ 16 kHz mono Float32 PCM
+→ mel spectrogram frontend
+→ Conformer encoder
+→ RNNT greedy decoder
+→ SentencePiece tokenizer
+→ text
+```
+
+The model bundle includes frontend assets so that native runtimes can reproduce the original preprocessing without relying on Python audio libraries.
+
+## Frontend configuration
+
+| Parameter          | Value |
+| ------------------ | ----: |
+| Sample rate        | 16000 |
+| Channels           |     1 |
+| Number of mel bins |    64 |
+| FFT size           |   320 |
+| Window length      |   320 |
+| Hop length         |   160 |
+| Center             | false |
+| Mel scale          |   HTK |
+| Mel normalization  |  none |
+| Power              |   2.0 |
+
+The effective feature hop is 10 ms before encoder subsampling. The encoder uses a subsampling factor of 4, so one encoder frame corresponds approximately to 40 ms of audio.
+
+## Architecture details
+
+| Component                | Value                 |
+| ------------------------ | --------------------- |
+| Encoder type             | Conformer             |
+| Number of encoder layers | 16                    |
+| Model dimension          | 768                   |
+| Attention heads          | 16                    |
+| Attention type           | Rotary self-attention |
+| Convolution kernel size  | 5                     |
+| Subsampling              | Conv1D                |
+| Subsampling factor       | 4                     |
+| Prediction network       | RNNT predictor        |
+| Joint network            | RNNT joint            |
+| Decoding                 | Greedy RNNT           |
+| Tokenizer                | SentencePiece         |
+| Vocabulary size          | 1024                  |
+| Blank ID                 | 1024                  |
+| Output classes           | 1025                  |
+
+## Validation
+
+The conversion was validated against the original PyTorch/Hugging Face model using tensor-level golden references.
+
+Validated components include:
+
+* audio frontend
+* mel spectrogram
+* pre-encoder
+* Conformer feed-forward blocks
+* rotary self-attention
+* Conformer convolution block
+* full Conformer layer
+* encoder stack
+* RNNT predictor
+* RNNT joint network
+* RNNT greedy decoding
+* SentencePiece tokenizer
+* full WAV-to-text pipeline
+
+### Selected validation results
+
+#### Mel frontend parity
+
+```text
+feature_shape: [64, 99]
+max_abs_diff: 0.0004234314
+mean_abs_diff: 2.8040542e-05
+```
+
+#### Encoder stack parity
+
+```text
+stack_max_abs_diff: 2.5629997e-06
+stack_mean_abs_diff: 3.8420205e-07
+```
+
+#### Full encoder parity
+
+```text
+output_shape: [1, 768, 25]
+max_abs_diff: 2.682209e-06
+mean_abs_diff: 4.0401252e-07
+```
+
+#### End-to-end smoke test
+
+A short Russian WAV sample was used to verify end-to-end decoding against the Python reference implementation.
+
+The native runtime and the Python reference produced identical text for the same input audio.
+
+The audio fixture is not included in this model repository. It is used only for runtime validation.
+
+## Performance
+
+Benchmarks were measured on Apple M1 Max with a native Apple MLX runtime in release mode.
+
+### Short audio benchmark
+
+| Runtime                  | Audio duration | Total time |    RTF |           Speed |
+| ------------------------ | -------------: | ---------: | -----: | --------------: |
+| Native Apple MLX runtime |           ~6 s |   ~0.168 s | ~0.028 | ~35.8× realtime |
+| Python reference         |           ~6 s |   ~0.701 s | ~0.117 |  ~8.6× realtime |
+
+The native runtime was approximately 4.2× faster than the Python reference in this short-audio warm benchmark.
+
+### Long-form benchmark
+
+Long-form audio is processed in chunks to keep memory usage predictable and enable efficient transcription on Apple devices.
+
+| Metric                   |           Value |
+| ------------------------ | --------------: |
+| Audio duration           |       911.252 s |
+| Audio duration           |     15 min 11 s |
+| Chunk size               |          20.0 s |
+| Chunk count              |              46 |
+| Total transcription time |       24.2145 s |
+| Real-time factor         |         0.02657 |
+| Speed                    | ~37.6× realtime |
+| Peak resident memory     |        ~1.15 GB |
+
+### Long-form stage breakdown
+
+| Stage                |     Time |  Share |
+| -------------------- | -------: | -----: |
+| Audio load           |  0.019 s |  ~0.1% |
+| Mel frontend         |  5.993 s | ~24.8% |
+| Model total          | 18.203 s | ~75.2% |
+| Encoder              |  5.463 s | ~22.6% |
+| RNNT greedy decoding | 12.736 s | ~52.6% |
+| RNNT decoder         |  1.821 s |  ~7.5% |
+| RNNT joint           | 10.680 s | ~44.1% |
+| RNNT readback        |  0.169 s |  ~0.7% |
+| Tokenizer            |  0.003 s |  ~0.0% |
+
+The current main runtime bottleneck is the RNNT joint network during greedy decoding.
+
+### Memory
+
+| Scenario                                  | Peak RSS |
+| ----------------------------------------- | -------: |
+| Native Apple MLX runtime, short audio     | ~1.10 GB |
+| Native Apple MLX runtime, long-form audio | ~1.15 GB |
+| Python reference, short audio             | ~1.76 GB |
+
+## Long-form transcription
+
+Long-form audio is intended to be processed in chunks.
+
+Recommended initial long-form settings:
+
+```text
+chunk_seconds: 20
+overlap_seconds: 0-2
+sample_rate: 16000
+channels: mono
+```
+
+Future runtimes may use VAD, overlap merging, and timestamp-aware segmentation for improved long-form quality.
+
+## Limitations
+
+* This bundle is optimized for native Apple MLX runtimes.
+* Long audio should be processed in chunks.
+* Current validation focuses on numerical parity and runtime behavior.
+* Word-level timestamps are not included in the model bundle itself.
+* Diarization is not included.
+* This repository contains model assets only, not application code or SDK source code.
+
+## Relation to the original model
+
+This bundle is a native Apple MLX runtime conversion of:
+
+```text
+ai-sage/GigaAM-v3
+revision: e2e_rnnt
+```
+
+No additional training or fine-tuning was performed.
+
+The conversion preserves the original Conformer RNN-T architecture, SentencePiece tokenizer layout, and preprocessing configuration, while packaging the model assets for native offline inference on iOS, iPadOS, and macOS.
+
+## License
+
+This model bundle follows the license terms of the original `ai-sage/GigaAM-v3` model.
+
+License: MIT
+
+## Attribution
+
+If you use this bundle, please also reference the original GigaAM model:
+
+```text
+ai-sage/GigaAM-v3
+```
+
+## Summary
+
+GigaAM v3 MLX provides a native Apple MLX model bundle for offline Russian ASR.
+
+It is intended for local, private, on-device speech recognition on Apple platforms without requiring Python or server-side inference at runtime.

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+  "name": "GigaAM v3 MLX",
+  "bundle_id": "gigaam-v3-mlx",
+  "source_model": "ai-sage/GigaAM-v3",
+  "source_revision": "e2e_rnnt",
+  "architecture": "conformer-rnnt",
+  "model_type": "rnnt",
+  "precision": "fp16",
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+  "runtime_target": "native-apple",
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+  "target_platforms": [
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+    "iPadOS",
+    "macOS Apple Silicon"
+  ]
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