SLM-SS

Model weights and matching configurations for the l_s and l_l SLM-SS autoregressive speech separators.

Code, installation and inference: Herobrinelth/SLM-SS-Inference.

Variant	Encoder	AR layers	AR hidden size	Checkpoint
l_s	WavLM Large	10	512	SLM-SS/checkpoints/l_s.ckpt
l_l	WavLM Large	12	1024	SLM-SS/checkpoints/l_l.ckpt

Both checkpoints contain model tensors and configuration, including fine-tuned WavLM parameters. They contain no optimizer or learning-rate scheduler state.

From the root of the cloned GitHub repository:

hf download Herobrinelth/SLM-SS \
  --include "SLM-SS/checkpoints/*.ckpt" "SLM-SS/configs/*.yaml" \
  --local-dir .

Third-party models

This model repository distributes only the two SLM-SS checkpoints and their YAML configurations. Obtain the codec and evaluation models directly from their respective upstream projects:

• WavTokenizer: novateur/WavTokenizer-large-speech-75token, wavtokenizer_large_speech_320_v2.ckpt.
• UTMOS22: the official sarulab-speech/UTMOS-demo Space provides epoch=3-step=7459.ckpt and wav2vec_small.pt.
• Fairseq wav2vec 2.0: the self-supervised backbone used by UTMOS.
• DNSMOS: DNSMOS/DNSMOS/sig_bak_ovr.onnx.
• ESPnet / OWSM: espnet/owsm_v3.1_ebf for WER evaluation.

Follow the GitHub README's Third-party model downloads section for exact download locations and the local checkpoint-conversion commands. Upstream WavTokenizer and UTMOS checkpoints need conversion before use with this repository's tensor-only loaders. The conversion preserves the downloaded originals and refuses to overwrite existing files.

Third-party models remain subject to their respective upstream terms.