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README.md

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+---
+license: mit
+language:
+  - en
+  - he
+tags:
+  - vocoder
+  - audio
+  - speech
+  - streaming
+  - vocos
+base_model: charactr/vocos-mel-24khz
+---
+
+# Vocos (mel, 24 kHz) with causal streaming support
+
+Causal fine-tune of [charactr/vocos-mel-24khz](https://huggingface.co/charactr/vocos-mel-24khz) for real-time streaming inference.
+Mel frames go in chunk by chunk, audio comes out with ~85 ms algorithmic latency (4-frame lookahead + ISTFT overlap-add),
+numerically identical to the offline forward pass.
+
+Code, streaming inference (`VocosStreamer`), and training setup: **https://github.com/thewh1teagle/vocos**
+
+## Model details
+
+- Backbone: `VocosBackbone` (dim 512, intermediate 1536, 8 ConvNeXt layers) with `causal=True`, `lookahead_frames=4`
+- Head: `ISTFTHead`, n_fft 1024, hop 256, padding "same"
+- Features: 100-bin mel, 24 kHz
+- Initialized from the pretrained non-causal `charactr/vocos-mel-24khz` weights (weight-compatible)
+
+## Training data
+
+| Phase | Data | Steps |
+|---|---|---|
+| 1 | LibriTTS-R (train-clean, 148,694 files, ~460 h) | 0 → 235k |
+| 2 | LibriTTS-R + 20% Hebrew podcast speech (37,174 segments, ~76 h, quality-filtered, upsampled 16→24 kHz) | 235k → current |
+
+Trained with the standard Vocos GAN objective (multi-period + multi-resolution discriminators, mel + adversarial + feature-matching losses),
+batch 16, 16384-sample crops. This checkpoint is a full PyTorch Lightning checkpoint (~epoch 14, step ~277k) including generator,
+discriminators, and optimizer states — usable both for inference (filter the `feature_extractor.`/`backbone.`/`head.` keys) and to
+resume training.
+
+## Usage
+
+See [examples/streaming_infer_ckpt.py](https://github.com/thewh1teagle/vocos/blob/main/examples/streaming_infer_ckpt.py)
+for loading this checkpoint and streaming audio chunk by chunk.

last.ckpt

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+oid sha256:ccd8bfedd6511cb38278f620e4d441ec62808de65e3106c924c3c5a05fd67088
+size 673556924