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MLX-native port of Supertone's Supertonic 3 multilingual TTS. Runs the full flow-matching + classifier-free-guidance pipeline at ~x100 realtime on Apple Silicon, with audio cosine 1.0 vs the cached MLX path and cosine 0.98 vs the upstream ONNX Runtime reference. Weights are hosted at https://huggingface.co/ambassadia/supertonic-3-mlx and auto-downloaded on first use; this repository ships the port code, the model card, audio samples, and a zero-config setup_and_test.sh. Install: pip install git+https://gitea.tavportal.com/olivier/supertonic-3-mlx.git Quick test: git clone https://gitea.tavportal.com/olivier/supertonic-3-mlx.git cd supertonic-3-mlx && ./setup_and_test.sh Licenses (dual): model weights = BigScience Open RAIL-M (Section 4 propagation), port code = Apache-2.0. See LICENSE, LICENSE-CODE, NOTICE. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
40 lines
1.9 KiB
Plaintext
40 lines
1.9 KiB
Plaintext
supertonic-3-mlx
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This release is a derivative of the upstream Supertone Supertonic 3
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text-to-speech model and consists of two artefact classes governed by
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two different licenses:
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1. The model weights (under ./weights/*.safetensors) are released under
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the BigScience Open RAIL-M License. The full text is in ./LICENSE and
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was copied verbatim from
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https://huggingface.co/Supertone/supertonic-3/blob/main/LICENSE
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The Attachment A use restrictions (Section 5 + Attachment A clauses
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(a)–(m)) apply to all downstream use of the model and of any output
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generated by the model.
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2. The MLX port code (under ./src/supertonic_3_mlx/) is released under
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the Apache License, Version 2.0. The full text is in ./LICENSE-CODE.
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Attribution and modifications statement (BigScience Open RAIL-M Section 4.c):
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Copyright (c) 2026 Supertone Inc. — original model weights and reference
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Python/ONNX implementation. Distributed at
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https://huggingface.co/Supertone/supertonic-3
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Copyright (c) 2026 Olivier Dupont — MLX-native port code, weight format
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conversion (ONNX → safetensors via the 3-stage extractor in
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``src/supertonic_3_mlx/pipeline.py:_convert_onnx``), and pipeline
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optimisations (``mx.compile`` of the CFG Euler loop, cross-attention
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K/V cache shared across the 5 Euler steps). Distributed at
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https://huggingface.co/ambassadia/supertonic-3-mlx
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The MLX port does not modify the model's learned parameters in any
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semantic sense — the only weight-level transformation is a tensor-shape
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re-layout to match the MLX memory model (e.g. depthwise Conv1d
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``(C, 1, K)`` → ``(C, K, 1)``). Bit-identical audio output to the
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upstream ONNX Runtime reference is preserved up to FP32 accumulation
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noise (cosine ≥ 0.98 on the full pipeline, cosine = 1.00 on the vocoder).
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No use of the Supertone trademarks, logos, or trade dress is asserted or
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permitted by this release (BigScience Open RAIL-M Section 8).
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