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README.md

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+---
+license: mit
+---
+
+# DAC Encoder ONNX (Voice Cloning)
+
+ONNX export of the **encoder + quantizer** from [ibm-research/DAC.speech.v1.0](https://huggingface.co/ibm-research/DAC.speech.v1.0).
+
+## Purpose
+
+Used for on-device voice cloning with OuteTTS 1.0. Encodes reference audio into discrete codebook indices
+that condition the OuteTTS model during synthesis.
+
+## Model Details
+
+- **Source**: `weights_24khz_1.5kbps_v1.0.pth` from `ibm-research/DAC.speech.v1.0`
+- **Components**: Encoder (21.5M params) + ResidualVectorQuantize (53K params)
+- **Input**: `(batch, 1, samples)` float32 PCM at 24kHz, values in [-1, 1]
+- **Output**: `(batch, 2, frames)` int64 codes, values 0-1023
+- **Frame rate**: 75 frames per second (320x downsampling)
+- **Codebooks**: 2 quantizers, each with 1024 entries
+- **Export**: PyTorch 2.10 → ONNX opset 18, verified with ONNX Runtime 1.25
+
+## Usage
+
+```python
+import onnxruntime as ort
+import numpy as np
+
+sess = ort.InferenceSession("dac_encoder_24khz.onnx")
+
+# 1 second of audio at 24kHz
+audio = np.random.randn(1, 1, 24000).astype(np.float32) * 0.1
+
+codes = sess.run(None, {"audio": audio})
+# codes[0].shape = (1, 2, 75) — 2 codebooks, 75 frames
+```
+
+## Integration with OuteTTS 1.0
+
+The codes map to special tokens in the OuteTTS 1.0 vocabulary:
+- Codebook 1: `<|c1_0|>` through `<|c1_1024|>`
+- Codebook 2: `<|c2_0|>` through `<|c2_1024|>`
+
+These are interleaved per frame to create the speaker conditioning prompt.
+
+## License
+
+MIT (same as DAC)