| --- |
| license: mit |
| base_model: inclusionAI/Ming-3.0-tts-tiny |
| base_model_relation: quantized |
| pipeline_tag: text-to-speech |
| language: [zh, en, de, es, fr, ko, ja] |
| tags: [glade, mlx, coreai, apple-silicon, neural-engine, ming, text-to-speech] |
| --- |
| |
| # Ming 3.0 TTS Tiny for Glade |
|
|
| Hybrid conversion of [Ming 3.0 TTS Tiny](https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny). |
| The **7.9B-parameter mixture-of-experts language backbone** runs through Swift |
| MLX on the GPU; Core AI handles audio aggregation, flow inference and AudioVAE. |
| The complete checkpoint contains **8,980,997,932 learned parameters** before |
| quantization, excluding registered buffers and the separately distributed CAMP+ |
| speaker encoder. Output is **44.1 kHz mono**. Despite the name, this is a large |
| model: this conversion is qualified on a **16 GB M3 MacBook Air**. |
|
|
| ## Using the bundle |
|
|
| Download one repository snapshot and keep the language, audio and speaker |
| directories together. The optional `GladeMingTiny` product exposes |
| `MingTinyModelBundle` and `MingTinySynthesizer`, including ordinary-text requests, |
| the upstream task prompts and the common ordered TTS session interface. Python |
| is not an application dependency. Core AI requires macOS 27 or iOS 27; iPhone |
| execution and memory fit are not qualified here. |
|
|
| ```sh |
| hf download coder543/ming-3.0-tts-tiny-glade --local-dir ./ming-tiny |
| glade tts --model ./ming-tiny --text "Welcome to the glade." \ |
| --voice US_James --output speech.wav |
| ``` |
|
|
| The built-in Chinese/English presets require no reference recording. Ordinary |
| prompted speech instead requires reference audio **and its matching transcript**. |
| Speaker-only controls, voice design, free-form instructions, paralinguistic |
| speech and ordered multi-speaker scripts retain their separate upstream task |
| contracts. Seven language controls are supported: Chinese, English, German, |
| Spanish, French, Korean and Japanese. No voice recording is bundled here. |
|
|
| Each request produces a complete upstream utterance, preserving its silence. |
| Online delivery orders completed requests; it does not stream unfinished latent |
| frames. The default phased policy releases language weights before waveform |
| rendering. Resident execution is also available. Memory mapping lets the OS |
| manage file-backed pages; it does not remove the language model's working set. |
|
|
| ## Representation and runtime contract |
|
|
| - `language/`: aligned, memory-mapped language weights. Routed experts use affine |
| 4-bit groups of 64. Other learned matrices retain BF16; recurrent state, router |
| weights and decay parameters use FP32. The unused vocabulary head is omitted |
| because speech consumes final hidden states. No additional training is applied. |
| - `audio/`: INT8-weight/FP16-activation Core AI flow and aggregation graphs, |
| AudioVAE reference encoder/decoder graphs and host reconstruction resources. |
| - `speaker/`: the original CAMP+ weights and native MLX network description, |
| with the source Kaldi filterbank and full-reference pooling conventions. |
| - The 24 language layers alternate three Kimi Delta Attention layers with one |
| multi-latent attention layer. The first feed-forward layer is dense; later |
| layers select eight of 128 routed experts and add a shared expert. |
| - Prompt and generated positions share a 4,096-position budget. The original |
| old-shift generation loop retains its terminal latent and emits 13–250 latents |
| at 12.5 Hz. The common interface reports an unfinished capped utterance as a |
| runout rather than presenting it as complete speech. |
| - The sampler uses ten EPSS Euler steps, guidance |
| `unconditional + 2 * (conditional - unconditional)`, sigma 0.25, temperature 0 |
| and sway -1. Noise/state are FP32; velocity, guidance and time increments |
| reproduce the original BF16 rounding boundaries. Metadata records the complete |
| sampler contract and default seeds. |
|
|
| ## Measured Mac performance |
|
|
| M3 MacBook Air, 16 GB, macOS 27.0.1; Release Glade runtime, `US_James`, seed 1234, |
| phased execution. The complete ordinary-text input is: |
|
|
| > Sunlight pours through the trees. A gentle breeze moves across the clearing. Welcome to Glade. |
|
|
| One same-text warmup precedes three measured passes. Initial preparation and WAV |
| writing are excluded; recurring phase loads, text preparation, language/flow |
| generation and waveform reconstruction are included. |
|
|
| | Pass | Audio | Generation | RTFx | |
| | --- | ---: | ---: | ---: | |
| | 1 | 6.72 s | 14.895 s | 0.451× | |
| | 2 | 6.72 s | 18.717 s | 0.359× | |
| | 3 | 6.72 s | 13.535 s | 0.497× | |
| | Median | 6.72 s | 14.895 s | **0.451×** | |
|
|
| A separate hardware trace confirms GPU language prefill/decode and ANE execution |
| for all 840 flow, 83 aggregation and 22 waveform-decoder calls. No GPU intervals |
| appear within those audio graph scopes. The capture is excluded from timing. |
| ANE attribution uses exact call counts and temporal containment; these events do |
| not measure arithmetic or bandwidth utilization. |
|
|
| The mapped language file is **5.8 GB** (decimal). MLX reports peaks of **5.9 GB |
| active** and **0.9 GB cached** allocations, sampled independently. These cannot |
| be added into a measured simultaneous peak. The process physical-footprint plus |
| Neural Engine no-footprint ledger peaks at 1.4 GB, but **does not cover the |
| mapped GPU model's complete memory requirement**. |
|
|
| ## Qualification and limits |
|
|
| Numerical controls cover original tasks/tokenization, speaker features/network |
| output, KDA/MLA recurrent states, flow sampling, reference conditioning and |
| waveform reconstruction. Original-code controls use the exact packed expert |
| values to distinguish quantization from native implementation differences. |
| Retained final-condition RMS differences are 3.31–12.42% across the examined |
| language steps/prefill partitions. Discrete expert routing can amplify small |
| arithmetic differences; internal RMS is not a perceptual speech-quality score. |
|
|
| Cohere Transcribe recovers every source word from the short native English |
| output. Repeated runs produce identical PCM, so they are one distinct audio |
| check. Separate reference-conditioned checks recover the intended English, |
| Chinese and German text. Reference choice matters: one German sentence with |
| an English synthetic reference stops early in both Glade and the original BF16 |
| implementation, while other tested references complete it. These checks do not |
| qualify every voice/language combination or long-form generation. No smartphone |
| performance or memory-fit claim is made. |
|
|
| ## Attribution |
|
|
| Checkpoint revision: `b170071302cb0b06435ea784787b119f40a08be8`. |
| Source implementation: [inclusionAI/Ming-3.0-tts](https://github.com/inclusionAI/Ming-3.0-tts), |
| revision `e999c39fa676722aaa9ea29b5aa19a90525b271f`. |
| Weights and source use MIT. `LICENSE`, `NOTICE` and `CODE-LICENSE` retain the |
| Ming, FLA, CAMP+/3D-Speaker, TorchAudio and Kaldi credits and license texts. |
| Core AI assets omit authoring debug locations while preserving graph signatures |
| and operation statistics. Device specialization caches are not distributed. |
|
|
