mirror of
https://github.com/cloudstack-llc/mlx-knife.git
synced 2026-07-01 20:44:14 -04:00
Release 2.0.4-beta.10: Audio PyPI fix (tiktoken workaround complete)
Audio/Whisper works with pip install - no Git workaround needed. See CHANGELOG.md for details. Tested: 647 passed, 11 skipped (Python 3.10-3.12)
This commit is contained in:
The BROKE Cluster Team
+2
-1
@@ -4,7 +4,7 @@ venv31?/
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venv_*/
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venv-*/
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test_env*/
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test_results*.log
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test-install-venv/
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mypy_*.log
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ruff_*.log
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*/__pycache__/*
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@@ -29,6 +29,7 @@ ML-workspaces/
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# Test artifacts (generated reports)
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*_report.json
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test_results_3_*.log
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test-img-collection/
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small-img-collection
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benchmarks/reports/*.html
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@@ -1,5 +1,45 @@
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# Changelog
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## [2.0.4-beta.10] - 2026-02-05
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> **⚠️ Upgrade Notice:** If you installed beta.9 from PyPI, audio transcription does not work due to an incomplete tiktoken patch. Please upgrade to beta.10: `pip install mlx-knife[all]==2.0.4b10`
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### Highlights
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**Audio Works Out-of-the-Box:** Complete tiktoken workaround for mlx-audio Issue #479. PyPI installation (`pip install mlx-knife[audio]`) now works without any manual Git installs. We bundle the full Whisper tokenizer (~340 LOC) from mlx-audio commit 9349644 and patch `Model.get_tokenizer()` at runtime to fallback to tiktoken when HuggingFace processor is unavailable.
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**Beta.9 Audio Bug:** The beta.9 release on PyPI had an incomplete tiktoken patch - it bundled the assets but didn't patch `Model.get_tokenizer()` (the class was incorrectly named `Whisper` instead of `Model`). This caused "Processor not found" errors with Whisper models.
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**Runtime Compatibility Accuracy:** Fixed `runtime_compatible` field in `mlxk list --health` showing incorrect values. Now properly gates embedding models, mis-routed audio models (Qwen3-Omni), transformers 5.x video_processor bugs, and unsupported tokenizers (Voxtral tekken.json).
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### Added
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- **Whisper Tokenizer Patch (mlx-audio Issue #479):**
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- New `mlxk2/audio/` module with `whisper_tokenizer.py` (~340 LOC)
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- Complete `Tokenizer` class and `get_tokenizer()` from mlx-audio commit 9349644
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- `audio_runner.py`: `_apply_whisper_tokenizer_patch()` patches `Model.get_tokenizer()`
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- tiktoken>=0.7.0 dependency (OpenAI core library, stable API)
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- Bundled tiktoken assets: `gpt2.tiktoken`, `multilingual.tiktoken`
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### Fixed
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- **Audio/Whisper with PyPI mlx-audio:** `pip install mlx-knife[audio]` now works without Git install workaround. The tiktoken regression in mlx-audio 0.3.1 (Issue #479) is fully patched.
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- **`runtime_compatible` Accuracy:**
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- Embedding models (Qwen3-Embedding) → Gate 5: "not supported by mlxk run"
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- Mis-routed audio models (Qwen3-Omni) → Gate 3a: "model_type not supported by mlx-audio"
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- transformers 5.x bugs (Qwen2-VL, MiMo-VL) → Gate 4a: "Video processor bug"
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- Voxtral tekken.json → Gate 3a: "tekken.json tokenizer not supported"
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### Changed
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- **Documentation:**
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- README.md: Audio installation simplified (no more Git install instructions)
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- ARCHITECTURE.md: Added "Runtime Compatibility Decision Tree" and Probe concept
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- ADR-020: Qwen3-Omni clarification (routes to mlx-vlm, not mlx-audio)
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---
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## [2.0.4-beta.9] - 2026-02-04
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### Highlights
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@@ -4,9 +4,9 @@
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<img src="https://github.com/mzau/mlx-knife/raw/main/mlxk-demo.gif" alt="MLX Knife Demo" width="900">
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</p>
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**Current Version: 2.0.4-beta.9** (Stable: 2.0.3)
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**Current Version: 2.0.4-beta.10** (Stable: 2.0.3)
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||||
[](https://github.com/mzau/mlx-knife/releases)
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||||
[](https://github.com/mzau/mlx-knife/releases)
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||||
[](https://www.apache.org/licenses/LICENSE-2.0)
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||||
[](https://www.python.org/downloads/)
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||||
[](https://support.apple.com/en-us/HT211814)
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@@ -17,6 +17,8 @@
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## Features
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> **⚠️ Beta.9 Audio Bug:** If you installed `mlx-knife[audio]==2.0.4b9` from PyPI, audio transcription fails with "Processor not found". Upgrade to beta.10: `pip install mlx-knife[all]==2.0.4b10`
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### What's New in 2.0.4 (Coming Soon - Currently Beta)
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- **Audio Transcription (STT)** - Whisper speech-to-text (`--audio` flag, `pip install mlx-knife[audio]`)
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- **Vision Models with EXIF Metadata** - Image analysis + automatic GPS/date/camera extraction visible to the model
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@@ -94,15 +96,15 @@ mlxk --version # → mlxk 2.0.3
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**Requirements:** macOS Apple Silicon, Python 3.9-3.12
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### 2. PyPI Beta (2.0.4-beta.9 - Text + Vision + Audio)
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### 2. PyPI Beta (2.0.4-beta.10 - Text + Vision + Audio)
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```bash
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pip install mlx-knife[all]==2.0.4b9
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mlxk --version # → mlxk 2.0.4b9
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pip install mlx-knife[all]==2.0.4b10
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mlxk --version # → mlxk 2.0.4b10
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```
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**Requirements:** macOS Apple Silicon, Python 3.10-3.12
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**Features:** Audio STT (Whisper), Vision with EXIF metadata, tiktoken workaround bundled
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**Features:** Audio STT (Whisper), Vision with EXIF metadata, full tiktoken workaround
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### 3. Developer Installation
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@@ -111,7 +113,7 @@ git clone https://github.com/mzau/mlx-knife.git
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cd mlx-knife
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pip install -e ".[all,dev,test]"
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mlxk --version # → mlxk 2.0.4b9
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mlxk --version # → mlxk 2.0.4b10
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pytest -v
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```
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@@ -449,18 +451,14 @@ mlxk convert <model> <output> --repair-index
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### Audio Transcription (Speech-to-Text)
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> **🎙️ New in beta.9:** Professional STT via dedicated Whisper models (mlx-audio backend). Backward compatible with Gemma-3n multimodal audio (mlx-vlm).
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> **🎙️ New in beta.9/10:** Professional STT via dedicated Whisper models (mlx-audio backend). Beta.10 fixes PyPI install (no Git workaround needed). Backward compatible with Gemma-3n multimodal audio (mlx-vlm).
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**Requirements:**
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- **Python 3.10+** (mlx-audio dependency)
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- **Installation:** (mlx-audio 0.3.1 PyPI has regression - install from Git):
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```bash
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pip install -e "git+https://github.com/Blaizzy/mlx-audio.git@9349644#egg=mlx-audio"
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pip install tiktoken
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```
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- **Installation:** `pip install mlx-knife[audio]` (tiktoken workaround bundled)
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- **No system dependencies:** MP3/WAV decoding via embedded libsndfile (no ffmpeg or Homebrew required)
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**✅ Recommended Models** (mlx-knife v2.0.4-beta.9):
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||||
**✅ Recommended Models** (mlx-knife v2.0.4-beta.10):
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||||
|
||||
| Model | Backend | Size | Duration | Notes |
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||||
|-------|---------|------|----------|-------|
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||||
@@ -1241,7 +1239,7 @@ Apache License 2.0 — see `LICENSE` (root) and `mlxk2/NOTICE`.
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||||
|
||||
<p align="center">
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||||
<b>Made with ❤️ by The BROKE team <img src="broke-logo.png" alt="BROKE Logo" width="30" align="middle"></b><br>
|
||||
<i>Version 2.0.4-beta.9 | February 2026</i><br>
|
||||
<i>Version 2.0.4-beta.10 | February 2026</i><br>
|
||||
<a href="https://github.com/mzau/broke-nchat">💬 Web UI: nChat - lightweight chat interface</a> •
|
||||
<a href="https://github.com/mzau/broke-cluster">🔮 Multi-node: BROKE Cluster</a>
|
||||
</p>
|
||||
|
||||
+2
-2
@@ -4,7 +4,7 @@ This document contains version-specific details, complete file listings, and imp
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## Current Status
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✅ **2.0.4-beta.9** — Audio transcription (Whisper via mlx-audio); Server `/v1/audio/transcriptions` endpoint; Probe/Policy architecture complete; Vision support Phase 1-3 (CLI + Server); Pipes/Memory-Aware; EXIF metadata; **Test Portfolio Separation complete**; Workspace Infrastructure (ADR-018 Phase 0a+0b+0c); Convert Operation (ADR-018 Phase 1); Resumable Clone; **Benchmark Schema v0.2.2** (Precise test timing).
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✅ **2.0.4-beta.10** — **Audio PyPI Fix** (tiktoken workaround complete); Runtime compatibility accuracy; Audio transcription (Whisper via mlx-audio); Server `/v1/audio/transcriptions` endpoint; Probe/Policy architecture complete; Vision support Phase 1-3 (CLI + Server); Pipes/Memory-Aware; EXIF metadata; **Test Portfolio Separation complete**; Workspace Infrastructure (ADR-018 Phase 0a+0b+0c); Convert Operation (ADR-018 Phase 1); Resumable Clone; **Benchmark Schema v0.2.2** (Precise test timing).
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|
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### Test Results (Official Reference)
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||||
|
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@@ -1614,7 +1614,7 @@ MLXK2_LIVE_PUSH=1 \
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|
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---
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### A5. Complete Test File Structure (2.0.4-beta.9)
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### A5. Complete Test File Structure (2.0.4-beta.10)
|
||||
|
||||
```
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scripts/
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@@ -235,10 +235,40 @@ def detect_audio_backend(probe: Path, config: Optional[Dict]) -> Optional[Backen
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| Whisper-* | `model_type: whisper*` | - | WhisperFeatureExtractor | MLX_AUDIO |
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| VibeVoice-ASR | Name heuristic | - | WhisperFeatureExtractor | MLX_AUDIO |
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| Gemma-3n | audio_config | vision_config (populated) | - | MLX_VLM |
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| Qwen3-Omni | audio_config | vision_config (populated) | - | MLX_VLM |
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**Note on Voxtral:** Config has `audio_config` but empty `vision_config: {}`. Priority 1 ensures it routes to mlx-audio (not mlx-vlm) per blaizzy's guidance. Works for both Original Mistral and mlx-knife converted variants.
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#### Qwen3-Omni Model Family: Special Considerations
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**Model Config Reality (mlx-community/Qwen3-Omni-30B-A3B-Instruct-4bit):**
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```json
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{
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"model_type": "qwen3_omni_moe",
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"audio_config": /* NOT PRESENT */,
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"vision_config": {} /* EMPTY dict */
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}
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```
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Plus `preprocessor_config.json` contains `"feature_extractor_type": "WhisperFeatureExtractor"`.
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**Current Detection Result:**
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- Priority 2 (audio_config + vision_config): **Skipped** (no audio_config)
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- Priority 4 (WhisperFeatureExtractor): **Matched** → `Backend.MLX_AUDIO`
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**Runtime Compatibility Issue:**
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- `model_type: "qwen3_omni_moe"` is NOT supported by mlx-lm
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- Therefore `runtime_compatible: False` with reason: "Model type qwen3_omni_moe not supported."
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**Status:** Qwen3-Omni is currently **not runnable** via mlx-knife because:
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1. mlx-audio doesn't support `qwen3_omni_moe` model architecture
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2. mlx-vlm doesn't support `qwen3_omni_moe` model architecture
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3. The model lacks `audio_config` so it doesn't route to MLX_VLM anyway
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|
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**Future:** When mlx-vlm or mlx-audio adds Qwen3-Omni support, the detection logic may need adjustment to:
|
||||
- Add Priority 1.5: `model_type == "qwen3_omni*"` → appropriate backend
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||||
- Or: Model converter creates `audio_config` during conversion
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|
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### Complete Routing Hierarchy
|
||||
|
||||
**Three-tier routing logic (run.py:452-620):**
|
||||
|
||||
@@ -22,6 +22,130 @@ All code paths follow this sequence:
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||||
|
||||
**Rationale:** Consistent probing and policy enforcement prevents silent fallbacks and ensures errors are visible at the earliest possible stage.
|
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|
||||
#### The Probe Concept
|
||||
|
||||
**User Perspective (UX):**
|
||||
|
||||
"Probe" is the step where mlx-knife reads a model's metadata files to understand:
|
||||
- What the model can do (text, vision, audio, embeddings)
|
||||
- Whether it's healthy (files present, formats correct)
|
||||
- Whether it can run on this system (backend availability, memory)
|
||||
|
||||
This happens automatically during `mlxk list`, `mlxk show`, `mlxk run`, and server model loading.
|
||||
|
||||
**Implementation:**
|
||||
|
||||
A "probe" is a `Path` object pointing to the model's snapshot directory containing:
|
||||
|
||||
```
|
||||
probe/
|
||||
├── config.json # Model architecture (model_type, vision_config, audio_config)
|
||||
├── tokenizer_config.json # Chat template, tokenizer settings
|
||||
├── tokenizer.json # Vocabulary (optional, may be sentencepiece)
|
||||
├── preprocessor_config.json # Vision/audio processor info (optional)
|
||||
└── *.safetensors # Weight files (checked for naming convention)
|
||||
```
|
||||
|
||||
**Key Probe Functions:**
|
||||
|
||||
| Function | Location | Purpose |
|
||||
|----------|----------|---------|
|
||||
| `detect_framework()` | `common.py` | MLX vs PyTorch vs GGUF |
|
||||
| `detect_model_type()` | `common.py` | chat, base, audio, embedding |
|
||||
| `detect_capabilities()` | `common.py` | text-generation, vision, audio, embeddings |
|
||||
| `detect_vision_capability()` | `common.py` | vision_config, preprocessor_config |
|
||||
| `detect_audio_capability()` | `common.py` | audio_config, WhisperFeatureExtractor |
|
||||
| `detect_audio_backend()` | `common.py` | MLX_AUDIO (STT) vs MLX_VLM (multimodal) |
|
||||
| `check_runtime_compatibility()` | `health.py` | Backend supports model_type |
|
||||
|
||||
**Probe vs Config:**
|
||||
|
||||
- `probe`: The `Path` to the snapshot directory (filesystem location)
|
||||
- `config`: The parsed `dict` from `config.json` (model metadata)
|
||||
|
||||
Both are passed to detection functions because some signals come from config fields, others from file presence.
|
||||
|
||||
#### Runtime Compatibility Decision Tree
|
||||
|
||||
The `runtime_compatible` field in `mlxk list --json` follows this decision tree:
|
||||
|
||||
```
|
||||
runtime_compatible?
|
||||
│
|
||||
├─[1] healthy == False?
|
||||
│ └─→ False (reason from health check)
|
||||
│
|
||||
├─[2] framework != "MLX"?
|
||||
│ └─→ False ("Incompatible framework: {framework}")
|
||||
│
|
||||
├─[3] has_audio AND audio_backend != None?
|
||||
│ │
|
||||
│ ├─[3a] audio_backend == MLX_AUDIO?
|
||||
│ │ │
|
||||
│ │ ├─ mlx-audio not installed?
|
||||
│ │ │ └─→ False ("mlx-audio not installed")
|
||||
│ │ │
|
||||
│ │ ├─ model_type NOT in [whisper*, voxtral]?
|
||||
│ │ │ └─→ False ("Model type '{x}' not supported by mlx-audio")
|
||||
│ │ │
|
||||
│ │ └─ tekken.json exists WITHOUT tokenizer.json?
|
||||
│ │ └─→ False ("Voxtral tekken.json tokenizer not supported")
|
||||
│ │
|
||||
│ └─[3b] audio_backend == MLX_VLM?
|
||||
│ │
|
||||
│ ├─ vision_runtime_compatibility(probe) fails?
|
||||
│ │ └─→ False (vision reason)
|
||||
│ │
|
||||
│ └─ check_runtime_compatibility(probe) fails?
|
||||
│ └─→ False ("Model type '{x}' not supported")
|
||||
│
|
||||
├─[4] has_vision?
|
||||
│ │
|
||||
│ ├─[4a] vision_runtime_compatibility(probe):
|
||||
│ │ │
|
||||
│ │ ├─ Python < 3.10?
|
||||
│ │ │ └─→ False ("Vision requires Python 3.10+")
|
||||
│ │ │
|
||||
│ │ ├─ mlx-vlm not installed?
|
||||
│ │ │ └─→ False ("mlx-vlm not installed")
|
||||
│ │ │
|
||||
│ │ └─ transformers 5.x + temporal_patch_size?
|
||||
│ │ └─→ False ("Video processor bug in transformers 5.x")
|
||||
│ │
|
||||
│ └─[4b] check_runtime_compatibility(probe):
|
||||
│ │
|
||||
│ └─ mlx-lm doesn't support model_type?
|
||||
│ └─→ False ("Model type '{x}' not supported")
|
||||
│
|
||||
├─[5] "embeddings" in capabilities?
|
||||
│ └─→ False ("Embedding models not supported by mlxk run")
|
||||
│
|
||||
└─[6] else (text-only models):
|
||||
│
|
||||
└─ check_runtime_compatibility(probe):
|
||||
│
|
||||
├─ Legacy weight format (weights.*.safetensors)?
|
||||
│ └─→ False ("Legacy format not supported by mlx-lm")
|
||||
│
|
||||
└─ mlx-lm doesn't support model_type?
|
||||
└─→ False ("Model type '{x}' not supported")
|
||||
|
||||
If all gates pass → True (runtime_compatible)
|
||||
```
|
||||
|
||||
**Gate Priority:**
|
||||
|
||||
| Priority | Gate | Checked For |
|
||||
|----------|------|-------------|
|
||||
| 1 | Health | All models |
|
||||
| 2 | Framework | All models |
|
||||
| 3 | Audio Backend | Audio-capable models |
|
||||
| 4 | Vision Backend | Vision-capable models (non-audio) |
|
||||
| 5 | Embeddings | Embedding models |
|
||||
| 6 | Text/LLM | Text-only models |
|
||||
|
||||
**Implementation:** `build_model_object()` in `common.py:599-634`
|
||||
|
||||
### 2. No Silent Fallbacks
|
||||
|
||||
If a model requires a specific capability but the corresponding backend is unavailable, the system **must fail explicitly**. Do not degrade to a lower-capability mode.
|
||||
|
||||
+1
-1
@@ -7,4 +7,4 @@ import warnings
|
||||
# Issue parity with 1.1.0 (Issue #22)
|
||||
warnings.filterwarnings('ignore', message='urllib3 v2 only supports OpenSSL 1.1.1+')
|
||||
|
||||
__version__ = "2.0.4b9"
|
||||
__version__ = "2.0.4b10"
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
"""Audio support module for mlxk2.
|
||||
|
||||
Contains workarounds for mlx-audio regressions, specifically:
|
||||
- Whisper tokenizer (tiktoken-based) for Issue #479
|
||||
"""
|
||||
|
||||
from .whisper_tokenizer import Tokenizer, get_tokenizer
|
||||
|
||||
__all__ = ["Tokenizer", "get_tokenizer"]
|
||||
@@ -0,0 +1,442 @@
|
||||
# Copyright 2023 Apple Inc.
|
||||
# SPDX-License-Identifier: MIT
|
||||
#
|
||||
# Whisper tokenizer implementation from mlx-audio commit 9349644 (2026-01-26).
|
||||
# This code was removed in PyPI release 0.3.1 (2026-01-29), breaking Whisper
|
||||
# transcription for models without HuggingFace processor.
|
||||
#
|
||||
# Bundled here as workaround for mlx-audio Issue #479.
|
||||
# See: https://github.com/Blaizzy/mlx-audio/issues/479
|
||||
#
|
||||
# Original source:
|
||||
# https://github.com/Blaizzy/mlx-audio/blob/9349644/mlx_audio/stt/models/whisper/tokenizer.py
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import base64
|
||||
import string
|
||||
from dataclasses import dataclass, field
|
||||
from functools import cached_property, lru_cache
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import tiktoken
|
||||
|
||||
# Try to import LANGUAGES from mlx-audio (still present in 0.3.1)
|
||||
# Fall back to our own definition if import fails
|
||||
try:
|
||||
from mlx_audio.stt.models.whisper.tokenizer import LANGUAGES, TO_LANGUAGE_CODE
|
||||
except ImportError:
|
||||
# Fallback: Full LANGUAGES dict from mlx-audio
|
||||
LANGUAGES = {
|
||||
"en": "english",
|
||||
"zh": "chinese",
|
||||
"de": "german",
|
||||
"es": "spanish",
|
||||
"ru": "russian",
|
||||
"ko": "korean",
|
||||
"fr": "french",
|
||||
"ja": "japanese",
|
||||
"pt": "portuguese",
|
||||
"tr": "turkish",
|
||||
"pl": "polish",
|
||||
"ca": "catalan",
|
||||
"nl": "dutch",
|
||||
"ar": "arabic",
|
||||
"sv": "swedish",
|
||||
"it": "italian",
|
||||
"id": "indonesian",
|
||||
"hi": "hindi",
|
||||
"fi": "finnish",
|
||||
"vi": "vietnamese",
|
||||
"he": "hebrew",
|
||||
"uk": "ukrainian",
|
||||
"el": "greek",
|
||||
"ms": "malay",
|
||||
"cs": "czech",
|
||||
"ro": "romanian",
|
||||
"da": "danish",
|
||||
"hu": "hungarian",
|
||||
"ta": "tamil",
|
||||
"no": "norwegian",
|
||||
"th": "thai",
|
||||
"ur": "urdu",
|
||||
"hr": "croatian",
|
||||
"bg": "bulgarian",
|
||||
"lt": "lithuanian",
|
||||
"la": "latin",
|
||||
"mi": "maori",
|
||||
"ml": "malayalam",
|
||||
"cy": "welsh",
|
||||
"sk": "slovak",
|
||||
"te": "telugu",
|
||||
"fa": "persian",
|
||||
"lv": "latvian",
|
||||
"bn": "bengali",
|
||||
"sr": "serbian",
|
||||
"az": "azerbaijani",
|
||||
"sl": "slovenian",
|
||||
"kn": "kannada",
|
||||
"et": "estonian",
|
||||
"mk": "macedonian",
|
||||
"br": "breton",
|
||||
"eu": "basque",
|
||||
"is": "icelandic",
|
||||
"hy": "armenian",
|
||||
"ne": "nepali",
|
||||
"mn": "mongolian",
|
||||
"bs": "bosnian",
|
||||
"kk": "kazakh",
|
||||
"sq": "albanian",
|
||||
"sw": "swahili",
|
||||
"gl": "galician",
|
||||
"mr": "marathi",
|
||||
"pa": "punjabi",
|
||||
"si": "sinhala",
|
||||
"km": "khmer",
|
||||
"sn": "shona",
|
||||
"yo": "yoruba",
|
||||
"so": "somali",
|
||||
"af": "afrikaans",
|
||||
"oc": "occitan",
|
||||
"ka": "georgian",
|
||||
"be": "belarusian",
|
||||
"tg": "tajik",
|
||||
"sd": "sindhi",
|
||||
"gu": "gujarati",
|
||||
"am": "amharic",
|
||||
"yi": "yiddish",
|
||||
"lo": "lao",
|
||||
"uz": "uzbek",
|
||||
"fo": "faroese",
|
||||
"ht": "haitian creole",
|
||||
"ps": "pashto",
|
||||
"tk": "turkmen",
|
||||
"nn": "nynorsk",
|
||||
"mt": "maltese",
|
||||
"sa": "sanskrit",
|
||||
"lb": "luxembourgish",
|
||||
"my": "myanmar",
|
||||
"bo": "tibetan",
|
||||
"tl": "tagalog",
|
||||
"mg": "malagasy",
|
||||
"as": "assamese",
|
||||
"tt": "tatar",
|
||||
"haw": "hawaiian",
|
||||
"ln": "lingala",
|
||||
"ha": "hausa",
|
||||
"ba": "bashkir",
|
||||
"jw": "javanese",
|
||||
"su": "sundanese",
|
||||
"yue": "cantonese",
|
||||
}
|
||||
|
||||
TO_LANGUAGE_CODE = {
|
||||
**{language: code for code, language in LANGUAGES.items()},
|
||||
"burmese": "my",
|
||||
"valencian": "ca",
|
||||
"flemish": "nl",
|
||||
"haitian": "ht",
|
||||
"letzeburgesch": "lb",
|
||||
"pushto": "ps",
|
||||
"panjabi": "pa",
|
||||
"moldavian": "ro",
|
||||
"moldovan": "ro",
|
||||
"sinhalese": "si",
|
||||
"castilian": "es",
|
||||
"mandarin": "zh",
|
||||
}
|
||||
|
||||
|
||||
# Path to bundled tiktoken assets in mlxk2
|
||||
_ASSETS_DIR = Path(__file__).parent.parent / "assets" / "whisper"
|
||||
|
||||
|
||||
@dataclass
|
||||
class Tokenizer:
|
||||
"""A thin wrapper around `tiktoken` providing quick access to special tokens."""
|
||||
|
||||
encoding: tiktoken.Encoding
|
||||
num_languages: int
|
||||
language: Optional[str] = None
|
||||
task: Optional[str] = None
|
||||
sot_sequence: Tuple[int, ...] = ()
|
||||
special_tokens: Dict[str, int] = field(default_factory=dict)
|
||||
|
||||
def __post_init__(self):
|
||||
for special in self.encoding.special_tokens_set:
|
||||
special_token = self.encoding.encode_single_token(special)
|
||||
self.special_tokens[special] = special_token
|
||||
|
||||
sot: int = self.special_tokens["<|startoftranscript|>"]
|
||||
translate: int = self.special_tokens["<|translate|>"]
|
||||
transcribe: int = self.special_tokens["<|transcribe|>"]
|
||||
|
||||
langs = tuple(LANGUAGES.keys())[: self.num_languages]
|
||||
sot_sequence = [sot]
|
||||
if self.language is not None:
|
||||
sot_sequence.append(sot + 1 + langs.index(self.language))
|
||||
if self.task is not None:
|
||||
task_token: int = transcribe if self.task == "transcribe" else translate
|
||||
sot_sequence.append(task_token)
|
||||
|
||||
self.sot_sequence = tuple(sot_sequence)
|
||||
|
||||
def encode(self, text, **kwargs):
|
||||
return self.encoding.encode(text, **kwargs)
|
||||
|
||||
def decode(self, token_ids: List[int], **kwargs) -> str:
|
||||
token_ids = [t for t in token_ids if t < self.timestamp_begin]
|
||||
return self.encoding.decode(token_ids, **kwargs)
|
||||
|
||||
def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str:
|
||||
"""Timestamp tokens are above other special tokens' id range and are ignored by decode().
|
||||
This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
|
||||
"""
|
||||
return self.encoding.decode(token_ids, **kwargs)
|
||||
|
||||
@cached_property
|
||||
def eot(self) -> int:
|
||||
return self.encoding.eot_token
|
||||
|
||||
@cached_property
|
||||
def transcribe(self) -> int:
|
||||
return self.special_tokens["<|transcribe|>"]
|
||||
|
||||
@cached_property
|
||||
def translate(self) -> int:
|
||||
return self.special_tokens["<|translate|>"]
|
||||
|
||||
@cached_property
|
||||
def sot(self) -> int:
|
||||
return self.special_tokens["<|startoftranscript|>"]
|
||||
|
||||
@cached_property
|
||||
def sot_lm(self) -> int:
|
||||
return self.special_tokens["<|startoflm|>"]
|
||||
|
||||
@cached_property
|
||||
def sot_prev(self) -> int:
|
||||
return self.special_tokens["<|startofprev|>"]
|
||||
|
||||
@cached_property
|
||||
def no_speech(self) -> int:
|
||||
return self.special_tokens["<|nospeech|>"]
|
||||
|
||||
@cached_property
|
||||
def no_timestamps(self) -> int:
|
||||
return self.special_tokens["<|notimestamps|>"]
|
||||
|
||||
@cached_property
|
||||
def timestamp_begin(self) -> int:
|
||||
return self.special_tokens["<|0.00|>"]
|
||||
|
||||
@cached_property
|
||||
def language_token(self) -> int:
|
||||
"""Returns the token id corresponding to the value of the `language` field."""
|
||||
if self.language is None:
|
||||
raise ValueError("This tokenizer does not have language token configured")
|
||||
|
||||
return self.to_language_token(self.language)
|
||||
|
||||
def to_language_token(self, language):
|
||||
if token := self.special_tokens.get(f"<|{language}|>", None):
|
||||
return token
|
||||
|
||||
raise KeyError(f"Language {language} not found in tokenizer.")
|
||||
|
||||
@cached_property
|
||||
def all_language_tokens(self) -> Tuple[int, ...]:
|
||||
result = []
|
||||
for token, token_id in self.special_tokens.items():
|
||||
if token.strip("<|>") in LANGUAGES:
|
||||
result.append(token_id)
|
||||
return tuple(result)[: self.num_languages]
|
||||
|
||||
@cached_property
|
||||
def all_language_codes(self) -> Tuple[str, ...]:
|
||||
return tuple(self.decode([_l]).strip("<|>") for _l in self.all_language_tokens)
|
||||
|
||||
@cached_property
|
||||
def sot_sequence_including_notimestamps(self) -> Tuple[int, ...]:
|
||||
return tuple(list(self.sot_sequence) + [self.no_timestamps])
|
||||
|
||||
@cached_property
|
||||
def non_speech_tokens(self) -> Tuple[int, ...]:
|
||||
"""Returns the list of tokens to suppress in order to avoid any speaker tags or
|
||||
non-speech annotations, to prevent sampling texts that are not actually spoken
|
||||
in the audio, e.g.
|
||||
|
||||
- (SPEAKING FOREIGN LANGUAGE)
|
||||
- [DAVID] Hey there,
|
||||
|
||||
keeping basic punctuations like commas, periods, question marks, etc.
|
||||
"""
|
||||
symbols = list('"#()*+/:;<=>@[\\]^_`{|}~')
|
||||
symbols += (
|
||||
"<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ".split()
|
||||
)
|
||||
|
||||
# symbols that may be a single token or multiple tokens depending on the tokenizer.
|
||||
# In case they're multiple tokens, suppress the first token, which is safe because:
|
||||
# These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
|
||||
# in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
|
||||
miscellaneous = set("\u2669\u266a\u266b\u266c\u266d\u266e\u266f")
|
||||
assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
|
||||
|
||||
# allow hyphens "-" and single quotes "'" between words, but not at the beginning
|
||||
result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]}
|
||||
for symbol in symbols + list(miscellaneous):
|
||||
for tokens in [
|
||||
self.encoding.encode(symbol),
|
||||
self.encoding.encode(" " + symbol),
|
||||
]:
|
||||
if len(tokens) == 1 or symbol in miscellaneous:
|
||||
result.add(tokens[0])
|
||||
|
||||
return tuple(sorted(result))
|
||||
|
||||
def split_to_word_tokens(self, tokens: List[int]):
|
||||
if self.language in {"zh", "ja", "th", "lo", "my", "yue"}:
|
||||
# These languages don't typically use spaces, so it is difficult to split words
|
||||
# without morpheme analysis. Here, we instead split words at any
|
||||
# position where the tokens are decoded as valid unicode points
|
||||
return self.split_tokens_on_unicode(tokens)
|
||||
|
||||
return self.split_tokens_on_spaces(tokens)
|
||||
|
||||
def split_tokens_on_unicode(self, tokens: List[int]):
|
||||
decoded_full = self.decode_with_timestamps(tokens)
|
||||
replacement_char = "\ufffd"
|
||||
|
||||
words = []
|
||||
word_tokens = []
|
||||
current_tokens = []
|
||||
unicode_offset = 0
|
||||
|
||||
for token in tokens:
|
||||
current_tokens.append(token)
|
||||
decoded = self.decode_with_timestamps(current_tokens)
|
||||
|
||||
if (
|
||||
replacement_char not in decoded
|
||||
or decoded_full[unicode_offset + decoded.index(replacement_char)]
|
||||
== replacement_char
|
||||
):
|
||||
words.append(decoded)
|
||||
word_tokens.append(current_tokens)
|
||||
current_tokens = []
|
||||
unicode_offset += len(decoded)
|
||||
|
||||
return words, word_tokens
|
||||
|
||||
def split_tokens_on_spaces(self, tokens: List[int]):
|
||||
subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens)
|
||||
words = []
|
||||
word_tokens = []
|
||||
|
||||
for subword, subword_tokens in zip(subwords, subword_tokens_list):
|
||||
special = subword_tokens[0] >= self.eot
|
||||
with_space = subword.startswith(" ")
|
||||
punctuation = subword.strip() in string.punctuation
|
||||
if special or with_space or punctuation or len(words) == 0:
|
||||
words.append(subword)
|
||||
word_tokens.append(subword_tokens)
|
||||
else:
|
||||
words[-1] = words[-1] + subword
|
||||
word_tokens[-1].extend(subword_tokens)
|
||||
|
||||
return words, word_tokens
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def get_encoding(name: str = "gpt2", num_languages: int = 99) -> tiktoken.Encoding:
|
||||
"""Load tiktoken encoding from bundled assets.
|
||||
|
||||
Uses assets from mlxk2/assets/whisper/ instead of mlx-audio's removed assets.
|
||||
"""
|
||||
vocab_path = _ASSETS_DIR / f"{name}.tiktoken"
|
||||
|
||||
if not vocab_path.exists():
|
||||
raise FileNotFoundError(
|
||||
f"Tiktoken vocabulary file not found: {vocab_path}\n"
|
||||
f"This is an mlx-audio Issue #479 workaround.\n"
|
||||
f"Expected assets in: {_ASSETS_DIR}"
|
||||
)
|
||||
|
||||
with open(vocab_path) as fid:
|
||||
ranks = {
|
||||
base64.b64decode(token): int(rank)
|
||||
for token, rank in (line.split() for line in fid if line)
|
||||
}
|
||||
|
||||
n_vocab = len(ranks)
|
||||
special_tokens = {}
|
||||
|
||||
specials = [
|
||||
"<|endoftext|>",
|
||||
"<|startoftranscript|>",
|
||||
*[f"<|{lang}|>" for lang in list(LANGUAGES.keys())[:num_languages]],
|
||||
"<|translate|>",
|
||||
"<|transcribe|>",
|
||||
"<|startoflm|>",
|
||||
"<|startofprev|>",
|
||||
"<|nospeech|>",
|
||||
"<|notimestamps|>",
|
||||
*[f"<|{i * 0.02:.2f}|>" for i in range(1501)],
|
||||
]
|
||||
|
||||
for token in specials:
|
||||
special_tokens[token] = n_vocab
|
||||
n_vocab += 1
|
||||
|
||||
return tiktoken.Encoding(
|
||||
name=vocab_path.name,
|
||||
explicit_n_vocab=n_vocab,
|
||||
pat_str=r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""",
|
||||
mergeable_ranks=ranks,
|
||||
special_tokens=special_tokens,
|
||||
)
|
||||
|
||||
|
||||
@lru_cache(maxsize=None)
|
||||
def get_tokenizer(
|
||||
multilingual: bool,
|
||||
*,
|
||||
num_languages: int = 99,
|
||||
language: Optional[str] = None,
|
||||
task: Optional[str] = None, # Literal["transcribe", "translate", None]
|
||||
) -> Tokenizer:
|
||||
"""Get a Whisper tokenizer.
|
||||
|
||||
Args:
|
||||
multilingual: Whether to use multilingual tokenizer (True for most models)
|
||||
num_languages: Number of languages supported (default 99)
|
||||
language: Language code (e.g., 'en', 'de') or None for auto-detect
|
||||
task: 'transcribe' or 'translate' or None
|
||||
|
||||
Returns:
|
||||
Tokenizer instance
|
||||
"""
|
||||
if language is not None:
|
||||
language = language.lower()
|
||||
if language not in LANGUAGES:
|
||||
if language in TO_LANGUAGE_CODE:
|
||||
language = TO_LANGUAGE_CODE[language]
|
||||
else:
|
||||
raise ValueError(f"Unsupported language: {language}")
|
||||
|
||||
if multilingual:
|
||||
encoding_name = "multilingual"
|
||||
language = language or "en"
|
||||
task = task or "transcribe"
|
||||
else:
|
||||
encoding_name = "gpt2"
|
||||
language = None
|
||||
task = None
|
||||
|
||||
encoding = get_encoding(name=encoding_name, num_languages=num_languages)
|
||||
|
||||
return Tokenizer(
|
||||
encoding=encoding, num_languages=num_languages, language=language, task=task
|
||||
)
|
||||
@@ -105,6 +105,67 @@ def _apply_tiktoken_patch():
|
||||
_apply_tiktoken_patch()
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# CRITICAL: Patch Model.get_tokenizer() to use our tiktoken tokenizer
|
||||
# ============================================================================
|
||||
# mlx-audio 0.3.1 (PyPI) removed the get_tokenizer() function that creates
|
||||
# tiktoken-based tokenizers. The Model.get_tokenizer() method now throws
|
||||
# ValueError if no HuggingFace processor is available.
|
||||
#
|
||||
# We patch Model.get_tokenizer() to fall back to our bundled tokenizer
|
||||
# (mlxk2.audio.whisper_tokenizer) when no processor is available.
|
||||
#
|
||||
# This MUST happen at module import time, before any Whisper model is loaded!
|
||||
# ============================================================================
|
||||
|
||||
def _apply_whisper_tokenizer_patch():
|
||||
"""Patch Model.get_tokenizer to fall back to our tiktoken tokenizer."""
|
||||
try:
|
||||
from mlx_audio.stt.models.whisper.whisper import Model as WhisperModel
|
||||
from mlxk2.audio.whisper_tokenizer import get_tokenizer
|
||||
|
||||
# Store original method (if it exists and isn't already patched)
|
||||
if hasattr(WhisperModel, "_mlxk_original_get_tokenizer"):
|
||||
# Already patched
|
||||
return
|
||||
|
||||
original_get_tokenizer = WhisperModel.get_tokenizer
|
||||
|
||||
def patched_get_tokenizer(self, language=None, task="transcribe"):
|
||||
"""Patched get_tokenizer with tiktoken fallback.
|
||||
|
||||
First tries the original method (uses HF Processor if available).
|
||||
Falls back to our bundled tiktoken-based tokenizer on failure.
|
||||
"""
|
||||
# Try original first (uses HF Processor if available)
|
||||
if hasattr(self, "_processor") and self._processor is not None:
|
||||
try:
|
||||
return original_get_tokenizer(self, language, task)
|
||||
except Exception:
|
||||
# HF Processor failed, fall through to tiktoken
|
||||
pass
|
||||
|
||||
# Fallback to our tiktoken-based tokenizer
|
||||
return get_tokenizer(
|
||||
self.is_multilingual,
|
||||
num_languages=getattr(self, "num_languages", 99),
|
||||
language=language,
|
||||
task=task,
|
||||
)
|
||||
|
||||
# Apply patch
|
||||
WhisperModel.get_tokenizer = patched_get_tokenizer
|
||||
WhisperModel._mlxk_original_get_tokenizer = original_get_tokenizer
|
||||
|
||||
except ImportError:
|
||||
# mlx-audio not installed - skip patching
|
||||
pass
|
||||
|
||||
|
||||
# Apply Whisper tokenizer patch immediately at module import
|
||||
_apply_whisper_tokenizer_patch()
|
||||
|
||||
|
||||
class AudioRunner:
|
||||
"""Wrapper around mlx-audio STT API for dedicated transcription models.
|
||||
|
||||
|
||||
@@ -426,25 +426,59 @@ def detect_capabilities(
|
||||
return caps
|
||||
|
||||
|
||||
def vision_runtime_compatibility() -> tuple[bool, Optional[str]]:
|
||||
"""Vision uses mlx-vlm backend; mark compatible only if available."""
|
||||
def vision_runtime_compatibility(probe: Optional[Path] = None) -> tuple[bool, Optional[str]]:
|
||||
"""Vision uses mlx-vlm backend; mark compatible only if available.
|
||||
|
||||
Args:
|
||||
probe: Optional path to model snapshot for video processor detection
|
||||
|
||||
Returns:
|
||||
(is_compatible, reason): reason is None if compatible
|
||||
"""
|
||||
if sys.version_info < (3, 10):
|
||||
return False, "Vision requires Python 3.10+ (mlx-vlm dependency)"
|
||||
spec = importlib.util.find_spec("mlx_vlm")
|
||||
if spec is None:
|
||||
return False, "mlx-vlm not installed (install extras: vision)"
|
||||
|
||||
# Gate 3: Check for transformers 5.x video_processor bug
|
||||
# transformers 5.0.x RC has a bug where video_processor_class_from_name()
|
||||
# fails with "argument of type 'NoneType' is not iterable" for models
|
||||
# with temporal_patch_size (video-capable models like Qwen2-VL)
|
||||
if probe is not None:
|
||||
try:
|
||||
import transformers
|
||||
tf_version = getattr(transformers, "__version__", "0.0.0")
|
||||
# Check if transformers 5.x (RC or early release with potential bugs)
|
||||
if tf_version.startswith("5."):
|
||||
preprocessor_path = probe / "preprocessor_config.json"
|
||||
if preprocessor_path.exists():
|
||||
preproc_data = _json.loads(preprocessor_path.read_text(encoding="utf-8", errors="ignore"))
|
||||
if isinstance(preproc_data, dict) and "temporal_patch_size" in preproc_data:
|
||||
return False, f"Video processor bug in transformers {tf_version} (use transformers<5.0 or wait for fix)"
|
||||
except Exception:
|
||||
pass # If check fails, proceed (may still work)
|
||||
|
||||
return True, None
|
||||
|
||||
|
||||
def audio_runtime_compatibility(backend: Backend) -> tuple[bool, Optional[str]]:
|
||||
def audio_runtime_compatibility(
|
||||
backend: Backend,
|
||||
probe: Optional[Path] = None,
|
||||
framework: str = "MLX"
|
||||
) -> tuple[bool, Optional[str]]:
|
||||
"""Audio runtime check based on backend (ADR-020).
|
||||
|
||||
Args:
|
||||
backend: Backend.MLX_AUDIO (Whisper/Voxtral) or Backend.MLX_VLM (Gemma-3n)
|
||||
backend: Backend.MLX_AUDIO (Whisper/Voxtral) or Backend.MLX_VLM (Gemma-3n, Qwen3-Omni)
|
||||
probe: Path to model snapshot (required for MLX_VLM model_type check)
|
||||
framework: Framework string (default "MLX")
|
||||
|
||||
Returns:
|
||||
(is_compatible, reason): reason is None if compatible
|
||||
"""
|
||||
from .health import check_runtime_compatibility
|
||||
|
||||
if sys.version_info < (3, 10):
|
||||
return False, "Audio requires Python 3.10+"
|
||||
|
||||
@@ -453,10 +487,47 @@ def audio_runtime_compatibility(backend: Backend) -> tuple[bool, Optional[str]]:
|
||||
spec = importlib.util.find_spec("mlx_audio")
|
||||
if spec is None:
|
||||
return False, "mlx-audio not installed (pip install mlx-knife[audio])"
|
||||
|
||||
# Gate 2: model_type must be supported by mlx-audio (Whisper, Voxtral only)
|
||||
# This catches mis-routed models like Qwen3-Omni that have WhisperFeatureExtractor
|
||||
# but are NOT STT models (they're multimodal with unsupported architecture)
|
||||
if probe is not None:
|
||||
config_path = probe / "config.json"
|
||||
if config_path.exists():
|
||||
try:
|
||||
config = _json.loads(config_path.read_text(encoding="utf-8", errors="ignore"))
|
||||
model_type = config.get("model_type", "")
|
||||
if isinstance(model_type, str):
|
||||
model_type_lower = model_type.lower()
|
||||
# Check if model_type is a known STT type
|
||||
if not any(stt in model_type_lower for stt in ["whisper", "voxtral"]):
|
||||
return False, f"Model type '{model_type}' not supported by mlx-audio (only Whisper/Voxtral)"
|
||||
except Exception:
|
||||
pass # If config can't be read, proceed (health check will catch it)
|
||||
|
||||
# Gate 3: Check for Voxtral tekken.json tokenizer bug (mlx-audio#450)
|
||||
# Voxtral uses tekken.json (Mistral tokenizer format) which mlx-audio can't convert properly
|
||||
tekken_path = probe / "tekken.json"
|
||||
tokenizer_path = probe / "tokenizer.json"
|
||||
if tekken_path.exists() and not tokenizer_path.exists():
|
||||
return False, "Voxtral tekken.json tokenizer not supported (mlx-audio#450, upstream fix pending)"
|
||||
|
||||
return True, None
|
||||
elif backend == Backend.MLX_VLM:
|
||||
# Multimodal audio (Gemma-3n) needs mlx-vlm
|
||||
return vision_runtime_compatibility()
|
||||
# Multimodal audio (Gemma-3n, Qwen3-Omni) needs mlx-vlm
|
||||
# Gate 1: mlx-vlm must be available (pass probe for video_processor bug check)
|
||||
vlm_ok, vlm_reason = vision_runtime_compatibility(probe)
|
||||
if not vlm_ok:
|
||||
return vlm_ok, vlm_reason
|
||||
|
||||
# Gate 2: mlx-lm must support model_type (text-only fallback mode)
|
||||
# This catches unsupported model_types like "qwen3_omni_moe"
|
||||
if probe is not None:
|
||||
text_ok, text_reason = check_runtime_compatibility(probe, framework)
|
||||
if not text_ok:
|
||||
return text_ok, text_reason
|
||||
|
||||
return True, None
|
||||
else:
|
||||
return False, "Unknown audio backend"
|
||||
|
||||
@@ -546,11 +617,12 @@ def build_model_object(hf_name: str, model_root: Path, selected_path: Optional[P
|
||||
runtime_reason = f"Incompatible framework: {framework}"
|
||||
elif has_audio and audio_backend is not None:
|
||||
# Audio models: check based on backend (ADR-020)
|
||||
runtime_compatible, runtime_reason = audio_runtime_compatibility(audio_backend)
|
||||
runtime_compatible, runtime_reason = audio_runtime_compatibility(audio_backend, probe, framework)
|
||||
elif has_vision:
|
||||
# Vision models: check BOTH backends for full chat+vision support
|
||||
# 1. mlx-vlm must be available (vision mode with images)
|
||||
vision_ok, vision_reason = vision_runtime_compatibility()
|
||||
# Pass probe for transformers 5.x video_processor bug detection
|
||||
vision_ok, vision_reason = vision_runtime_compatibility(probe)
|
||||
# 2. mlx-lm must support model_type (text-only mode without images)
|
||||
text_ok, text_reason = check_runtime_compatibility(probe, framework)
|
||||
|
||||
@@ -561,6 +633,10 @@ def build_model_object(hf_name: str, model_root: Path, selected_path: Optional[P
|
||||
runtime_compatible = False
|
||||
# Prefer text_reason as it's more specific (model_type not supported)
|
||||
runtime_reason = text_reason or vision_reason
|
||||
elif Capability.EMBEDDINGS.value in capabilities:
|
||||
# Embedding models: mlxk run doesn't support embeddings (future: mlxk embed)
|
||||
runtime_compatible = False
|
||||
runtime_reason = "Embedding models not supported by mlxk run (use mlxk embed)"
|
||||
else:
|
||||
runtime_compatible, runtime_reason = check_runtime_compatibility(probe, framework)
|
||||
|
||||
|
||||
@@ -382,7 +382,8 @@ def run_model(
|
||||
return error_result
|
||||
|
||||
if is_vision_model:
|
||||
compat, reason = vision_runtime_compatibility()
|
||||
# Pass model_path for transformers 5.x video_processor bug detection
|
||||
compat, reason = vision_runtime_compatibility(model_path)
|
||||
if not compat:
|
||||
error_msg = f"Model '{resolved_name}' is vision-capable but not runnable: {reason}"
|
||||
error_result = f"Error: {error_msg}"
|
||||
|
||||
+8
-9
@@ -66,19 +66,18 @@ dev = [
|
||||
"mypy>=1.5.0",
|
||||
]
|
||||
vision = [
|
||||
"mlx-vlm>=0.3.10", # Vision support (ADR-012)
|
||||
"mlx-vlm==0.3.10", # Pinned: stable, tested version (ADR-012)
|
||||
]
|
||||
audio = [
|
||||
# mlx-audio 0.3.1+ (tiktoken assets workaround bundled in mlxk2/assets/whisper/)
|
||||
# See Issue #479: https://github.com/Blaizzy/mlx-audio/issues/479
|
||||
"mlx-audio>=0.3.1",
|
||||
"tiktoken>=0.7.0", # Required by bundled tiktoken assets
|
||||
# mlx-audio pinned: 0.3.1 has tiktoken regression (Issue #479)
|
||||
# We bundle complete tiktoken workaround in mlxk2/audio/whisper_tokenizer.py
|
||||
"mlx-audio==0.3.1",
|
||||
"tiktoken>=0.7.0", # Required for Whisper tiktoken fallback
|
||||
]
|
||||
all = [
|
||||
"mlx-vlm>=0.3.10",
|
||||
# mlx-audio 0.3.1+ (tiktoken assets workaround bundled in mlxk2/assets/whisper/)
|
||||
"mlx-audio>=0.3.1",
|
||||
"tiktoken>=0.7.0", # Required by bundled tiktoken assets
|
||||
"mlx-vlm==0.3.10", # Pinned: stable, tested version
|
||||
"mlx-audio==0.3.1", # Pinned: tiktoken regression patched by mlxk2
|
||||
"tiktoken>=0.7.0",
|
||||
]
|
||||
|
||||
[tool.setuptools]
|
||||
|
||||
Reference in New Issue
Block a user