Duplicate from HeartMuLa/HeartCodec-oss-encoder

Co-authored-by: HeartMuLa <HeartMuLa@users.noreply.huggingface.co>

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LICENSE

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README.md

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+---
+license: apache-2.0
+tags:
+- audio
+- heartcodec
+- encoder
+---
+
+# HeartCodec OSS Encoder
+
+Encoder-only weights for HeartCodec audio tokenization. Pair this checkpoint
+with [HeartCodec-oss-20260123](https://huggingface.co/HeartMuLa/HeartCodec-oss-20260123),
+which supplies the shared RVQ quantizer and audio decoder. Together they support
+audio → eight token streams at 12.5 Hz → 48 kHz stereo reconstruction.
+
+The matching code and reconstruction examples are in
+[HeartMuLa/heartlib](https://github.com/HeartMuLa/heartlib).
+
+We hope this contribution will be useful to the music research community.
+
+## Installation and reconstruction
+
+Use Python 3.10, a CUDA-compatible PyTorch installation, and `ffmpeg` for MP3
+output. Install `heartlib` in a separate environment.
+
+```bash
+git clone https://github.com/HeartMuLa/heartlib.git
+cd heartlib
+pip install -e .
+hf download HeartMuLa/HeartCodec-oss-encoder --local-dir ./ckpt/HeartCodec-oss-encoder
+hf download HeartMuLa/HeartCodec-oss-20260123 --local-dir ./ckpt/HeartCodec-oss-20260123
+python examples/run_music_reconstruction.py \
+  --encoder_path ./ckpt/HeartCodec-oss-encoder \
+  --decoder_path ./ckpt/HeartCodec-oss-20260123 \
+  --input_path ./assets/reference.mp3 \
+  --save_path ./reconstructed.mp3
+```
+
+Replace `--input_path` to use your own authorized audio. MP3 is encoded directly from float32 output at
+320 kbps, without an intermediate PCM WAV. WAV output is available by selecting
+a `.wav` output path. Output is trimmed to the input duration.
+
+## Python API
+
+```python
+import torch
+from heartlib.heartcodec.modeling_heartcodec import HeartCodec
+
+codec = HeartCodec.from_encoder_decoder_pretrained(
+    "HeartMuLa/HeartCodec-oss-20260123",
+    "HeartMuLa/HeartCodec-oss-encoder",
+    dtype=torch.float32,
+).to("cuda").eval()
+
+# waveform: float32 tensor [samples], [1, samples], or [2, samples]
+# sample_rate: positive integer sampling rate of the input waveform
+# tokens = codec.tokenize(waveform, sample_rate, batch_size=1)
+# reconstructed = codec.detokenize(tokens, num_steps=10, guidance_scale=1.25)
+```
+
+Do not pass this encoder-only repository to `HeartCodec.from_pretrained()`.
+The existing decoder-only loading API remains supported. Encoder and decoder
+weights are strictly checked by the split-checkpoint loader.
+
+## Files
+
+- `encoder.safetensors`: 1,006 encoder tensors, approximately 2.15 GB;
+  original `encoder.*` names, shapes, dtypes and values are preserved.
+- `encoder_config.json`: feature extractor and query encoder configuration.
+- `config.json`: same content as `encoder_config.json`.
+- `SHA256SUMS`: weight-file SHA-256 checksum.
+- `LICENSE`: the source checkpoint's Apache-2.0 license.
+
+No decoder or RVQ weights are included here. The shared quantizer belongs to
+the paired decoder checkpoint; loading two independently trained quantizers
+is not supported.
+
+## Validation
+
+On an NVIDIA B300, the split encoder and published decoder completed a
+239.293-second stereo audio round trip using FP32, batch size 1, seed 42,
+10 decoder steps and guidance scale 1.25. Tokens had shape `[8, 2992]`.
+Output was finite, non-silent, 48 kHz stereo and matched the input duration.
+PyTorch peak allocated memory was approximately 12.65 GiB; peak reserved
+memory was approximately 15.41 GiB. These are measurements for one input and
+configuration, not minimum-memory requirements or an audio-quality benchmark.
+
+All exported encoder tensor bytes match the source checkpoint. The paired
+decoder's 818 tensors match the source decoder numerically; eight RVQ
+initialization flags differ only in stored shape (scalar versus `[1]`).
+
+## License
+
+Apache-2.0, as declared by the source checkpoint. See `LICENSE`.

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