Audiosronnx Callenhancer

--- license: cc-by-nc-4.0 library_name: audiosronnx pipeline_tag: audio-to-audio tags: - speech-restoration - bandwidth-extension - telephony - onnx - w2v-bert - dac --- # audiosronnx-callenhancer Pure-ONNX weights for the **`callenhancer`** engine in [audiosronnx](https://github.com/TigreGotico/audiosronnx) — call-centre / telephony speech **restoration**, 16 kHz → 48 kHz. This is an ONNX export of [Scicom-intl/CallEnhancer](https://huggingface.co/Scicom-intl/CallEnhancer): - `feature_extractor.int8.onnx` — the full 24-layer w2v-BERT 2.0 feature predictor (LoRA merged into the base weights), int8-quantized for CPU. Maps SeamlessM4T log-mel features `[1, T, 160]` → hidden states `[1, T, 1024]` at 50 Hz. - `decoder.onnx` — the 188 M-param DAC vocoder (fp32), `[1, 1024, T]` → `[1, 1, 960·T]` 48 kHz waveform. The SeamlessM4T log-mel front-end runs in numpy inside audiosronnx, so inference is onnxruntime-only (no torch). Each graph was validated against the original PyTorch model (feature extractor max abs err 1.9e-5, decoder 5.0e-6; numpy mel front-end 1.0e-5). ## Usage ```python from audiosronnx import load_sr sr = load_sr("callenhancer") sr.upscale_file("call_8k.wav", "restored_48k.wav") ``` ## License The model weights are **CC-BY-NC-4.0** (research / non-commercial), inherited from the upstream CallEnhancer release, which builds on Meta's w2v-BERT 2.0 and the Descript Audio Codec. This covers the model itself — audio you restore *with* it is your own.

Source context: 0 downloads · 0 likes · Pipeline audio-to-audio · Library audiosronnx · Repo TigreGotico/audiosronnx-callenhancer