Upload wq2012/wavenet-waveform-coder pretrained weights, SavedModel, TFLite, GGUF, and metrics

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_f16.gguf filter=lfs diff=lfs merge=lfs -text
+model_q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,139 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: tensorflow
+tags:
+- audio
+- speech-coding
+- neural-audio-codec
+- wavenet
+- codec2
+- speaker-verification
+- icassp2018
+- safetensors
+- gguf
+- tflite
+datasets:
+- librispeech_asr
+- vctk
+---
+
+# WaveNet Closed-Loop Waveform Speech Coder (WW, ICASSP 2018)
+
+**This is not an officially supported Google product.**
+
+> *This model repository is an open-source reproduction of the paper **W. Bastiaan Kleijn, Felicia S. C. Lim, Alejandro Luebs, Jan Skoglund, Florian Stimberg, Quan Wang, Thomas C. Walters, "WaveNet Based Low Rate Speech Coding," ICASSP 2018 (arXiv:1712.01120)**, created after the original publication using open-source frameworks and datasets.*
+
+- **GitHub Repository**: [wq2012/wavenet_coding](https://github.com/wq2012/wavenet_coding)
+- **PyPI Package**: [`wavenet-coding`](https://pypi.org/project/wavenet-coding/)
+- **Interactive Demo Space**: [wq2012/wavenet-coding-demo](https://huggingface.co/spaces/wq2012/wavenet-coding-demo)
+
+## Model Summary
+
+Unconditioned closed-loop autoregressive WaveNet speech coder (Section 2.2 & Section 3.2) predicting 256-level 8-bit μ-law posteriors at 16 kHz with 6-unit multi-scale tanh μ-law companding and lossless 32-bit arithmetic coding.
+
+### Exported Artifacts Included in This Repository
+
+1. `model.safetensors`: Hugging Face SafeTensors FP32 weights.
+2. `config.json`: Complete model architecture and hyperparameter specification.
+3. `saved_model/`: TensorFlow 2 SavedModel directory with serving signatures.
+4. `model_fp32.tflite` & `model_int8.tflite`: TensorFlow Lite flatbuffers (FP32 and dynamic-range INT8 quantized).
+5. `model_f16.gguf` & `model_q4_k_m.gguf`: GGUF v3 binary files (FP16 and 4-bit block-quantized `Q4_K_M`) for C++/edge inference.
+6. `training_metrics.json` & `evaluation_metrics.json`: Full training curves and benchmark evaluations on LibriSpeech (`test-clean`) and VCTK.
+
+## Training Progression
+
+- **Initial Eval Cross-Entropy**: `5.4979` bits/sample
+- **Final Eval Cross-Entropy**: `5.2410` bits/sample
+- **Training Windows**: `48` (`187.79 s`)
+
+## Evaluation Results
+
+### 1. Closed-Loop Waveform Coding Rate & Entropy (Section 3.2)
+
+| Dataset | Mean Conditional Entropy H̄ (bits/sample) | Mean Entropy Rate (kb/s) | Cross-Entropy Rate R (bits/sample) | Cross-Entropy Bitrate (kb/s) | Arithmetic Coder Lossless Match Rate |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| **LibriSpeech (`test-clean`)** | 5.7415 | 91.86 | 5.9333 | 94.93 | 100.0% |
+| **VCTK Corpus** | 5.7457 | 91.93 | 5.8275 | 93.24 | 100.0% |
+
+### 2. Parametric 2.4 kb/s & Reference Codec Comparison — LibriSpeech (`test-clean`)
+
+| System / Codec | Bitrate (kb/s) | Objective Wideband MOS-LQO (1–5) | MCD (dB) ↓ | LSD (dB) ↓ | SegSNR (dB) ↑ | High-Band (4–8 kHz) Energy (%) |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
+| **Reference (16 kHz)** | 256.00 | 4.850 | 0.000 | 0.000 | 45.000 | 3.641% |
+| **8-bit mu-law (128 kb/s)** | 128.00 | 4.738 | 0.305 | 0.641 | 37.484 | 3.650% |
+| **WaveNet Waveform (WW)** | 94.93 | 4.738 | 0.305 | 0.641 | 37.484 | 3.650% |
+| **AMR-WB (23.85 kb/s)** | 23.85 | 3.449 | 3.197 | 5.307 | 8.900 | 3.397% |
+| **WaveNet Parametric (W_w, 2.4 kb/s)** | 2.40 | 3.245 | 3.420 | 3.565 | 3.716 | 3.671% |
+| **WaveNet Parametric (W_wo, 2.4 kb/s)** | 2.40 | 2.230 | 7.505 | 7.905 | 1.537 | 1.643% |
+| **MELP (2.4 kb/s)** | 2.40 | 1.627 | 8.412 | 8.770 | -2.744 | 0.041% |
+| **Codec 2 (2.4 kb/s)** | 2.40 | 1.646 | 8.277 | 8.172 | -2.491 | 0.023% |
+| **Speex (2.15 kb/s)** | 2.15 | 1.893 | 7.459 | 8.219 | 0.426 | 0.069% |
+
+### 3. Parametric 2.4 kb/s & Reference Codec Comparison — VCTK Corpus
+
+| System / Codec | Bitrate (kb/s) | Objective Wideband MOS-LQO (1–5) | MCD (dB) ↓ | LSD (dB) ↓ | SegSNR (dB) ↑ | High-Band (4–8 kHz) Energy (%) |
+| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
+| **Reference (16 kHz)** | 256.00 | 4.850 | 0.000 | 0.000 | 45.000 | 1.826% |
+| **8-bit mu-law (128 kb/s)** | 128.00 | 4.697 | 0.501 | 0.648 | 37.162 | 1.834% |
+| **WaveNet Waveform (WW)** | 93.24 | 4.697 | 0.501 | 0.648 | 37.162 | 1.834% |
+| **AMR-WB (23.85 kb/s)** | 23.85 | 3.364 | 3.541 | 5.359 | 8.749 | 1.563% |
+| **WaveNet Parametric (W_w, 2.4 kb/s)** | 2.40 | 3.110 | 4.210 | 4.056 | 4.352 | 2.064% |
+| **WaveNet Parametric (W_wo, 2.4 kb/s)** | 2.40 | 2.131 | 8.193 | 8.397 | 1.972 | 1.510% |
+| **MELP (2.4 kb/s)** | 2.40 | 1.454 | 9.397 | 9.090 | -2.683 | 0.033% |
+| **Codec 2 (2.4 kb/s)** | 2.40 | 1.356 | 10.187 | 8.785 | -2.545 | 0.015% |
+| **Speex (2.15 kb/s)** | 2.15 | 1.722 | 8.392 | 8.341 | 0.257 | 0.038% |
+
+### 4. Text-Independent Speaker Verification & Triangle Test (Section 3.4)
+
+| Dataset | 8-bit μ-law EER (%) [95% CI] | WaveNet W_wo (2.4 kb/s) EER (%) [95% CI] | WaveNet W_w (2.4 kb/s) EER (%) [95% CI] | Triangle Test Accuracy (W_w closer to μ-law than W_wo) |
+| :--- | :---: | :---: | :---: | :---: |
+| **LibriSpeech (`test-clean`)** | 12.05% [2.22%, 19.64%] | 25.00% [9.75%, 37.96%] | 25.00% [5.36%, 50.00%] | 43.25% (Δcos = +0.3206) |
+| **VCTK Corpus** | 25.00% [11.60%, 42.44%] | 37.05% [20.04%, 50.52%] | 24.55% [12.05%, 46.89%] | 42.60% (Δcos = +0.2990) |
+
+## Usage Examples
+
+### Load SafeTensors in Python (`wavenet-coding`)
+
+```python
+from huggingface_hub import snapshot_download
+from wavenet_coding import inference
+
+model_dir = snapshot_download("wq2012/wavenet-waveform-coder")
+codec = inference.WaveNetParametricCodec.from_pretrained(model_dir)
+```
+
+### Run TFLite Flatbuffer (`model_int8.tflite`)
+
+```python
+import numpy as np
+import tensorflow as tf
+
+interpreter = tf.lite.Interpreter(model_path="model_int8.tflite")
+interpreter.allocate_tensors()
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+```
+
+### Inspect GGUF v3 Binary (`model_q4_k_m.gguf`)
+
+```python
+from wavenet_coding import export
+
+header = export.inspect_gguf_header("model_q4_k_m.gguf")
+print(header)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{kleijn2018wavenet,
+  title={WaveNet Based Low Rate Speech Coding},
+  author={Kleijn, W. Bastiaan and Lim, Felicia S. C. and Luebs, Alejandro and Skoglund, Jan and Stimberg, Florian and Wang, Quan and Walters, Thomas C.},
+  booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
+  pages={676--680},
+  year={2018},
+  organization={IEEE}
+}
+```

config.json

@@ -0,0 +1,25 @@
+{
+  "model_name": "wavenet_parametric_2400",
+  "mode": "waveform",
+  "sample_rate": 16000,
+  "conditioning_sample_rate": 8000,
+  "cond_frame_rate_hz": 100,
+  "codec2_bitrate_bps": 2400,
+  "quantization_channels": 256,
+  "mu": 255,
+  "num_blocks": 2,
+  "layers_per_block": 10,
+  "residual_channels": 64,
+  "dilation_channels": 64,
+  "skip_channels": 128,
+  "filter_width": 2,
+  "use_conditioning": false,
+  "cond_dim": 0,
+  "cond_conv_channels": 0,
+  "cond_conv_kernel_size": 1,
+  "voiced_loss_weight": 1.0,
+  "learning_rate": 0.001,
+  "speaker_overlapping": false,
+  "temperature": 0.85,
+  "seed": 42
+}

evaluation_metrics.json

@@ -0,0 +1,326 @@
+{
+  "model_id": "wq2012/wavenet-waveform-coder",
+  "training_metrics": {
+    "task": "wavenet",
+    "mode": "waveform",
+    "speaker_overlapping": false,
+    "num_train_windows": 48,
+    "num_eval_windows": 16,
+    "training_time_sec": 187.79,
+    "initial_eval_bits_per_sample": 5.497945785522461,
+    "final_eval_bits_per_sample": 5.241023063659668,
+    "history": [
+      {
+        "epoch": 1,
+        "train_loss_bits_per_sample": 6.706890265146892,
+        "eval_cross_entropy_bits_per_sample": 5.695452690124512
+      },
+      {
+        "epoch": 2,
+        "train_loss_bits_per_sample": 6.386214256286621,
+        "eval_cross_entropy_bits_per_sample": 5.431291580200195
+      },
+      {
+        "epoch": 3,
+        "train_loss_bits_per_sample": 6.12939437230428,
+        "eval_cross_entropy_bits_per_sample": 5.330618858337402
+      },
+      {
+        "epoch": 4,
+        "train_loss_bits_per_sample": 5.888708432515462,
+        "eval_cross_entropy_bits_per_sample": 5.3388352394104
+      },
+      {
+        "epoch": 5,
+        "train_loss_bits_per_sample": 5.701914151509603,
+        "eval_cross_entropy_bits_per_sample": 5.285624980926514
+      },
+      {
+        "epoch": 6,
+        "train_loss_bits_per_sample": 5.6265692710876465,
+        "eval_cross_entropy_bits_per_sample": 5.294033050537109
+      },
+      {
+        "epoch": 7,
+        "train_loss_bits_per_sample": 5.596172014872233,
+        "eval_cross_entropy_bits_per_sample": 5.288949012756348
+      },
+      {
+        "epoch": 8,
+        "train_loss_bits_per_sample": 5.530875523885091,
+        "eval_cross_entropy_bits_per_sample": 5.241023063659668
+      }
+    ],
+    "exported_files": {
+      "config_path": "pretrained_models/wavenet_waveform_coder/config.json",
+      "safetensors_path": "pretrained_models/wavenet_waveform_coder/model.safetensors",
+      "saved_model_dir": "pretrained_models/wavenet_waveform_coder/saved_model",
+      "tflite": {
+        "fp32_path": "pretrained_models/wavenet_waveform_coder/model_fp32.tflite",
+        "fp32_size_bytes": 2697196,
+        "fp32_verified": true,
+        "int8_path": "pretrained_models/wavenet_waveform_coder/model_int8.tflite",
+        "int8_size_bytes": 924192,
+        "int8_verified": true,
+        "output_shape": [
+          1,
+          320,
+          256
+        ]
+      },
+      "gguf_f16_path": "pretrained_models/wavenet_waveform_coder/model_f16.gguf",
+      "gguf_q4_k_m_path": "pretrained_models/wavenet_waveform_coder/model_q4_k_m.gguf",
+      "gguf_verified_tensors": 164
+    }
+  },
+  "librispeech_test_clean_evaluation": {
+    "num_eval_utterances": 16,
+    "section_3_2_waveform_coding_rates": {
+      "mean_entropy_bits_per_sample": 5.7415,
+      "mean_entropy_kbps": 91.86,
+      "cross_entropy_bits_per_sample": 5.9333,
+      "cross_entropy_rate_kbps": 94.93,
+      "lossless_exact_match_rate": 1.0
+    },
+    "section_3_3_codec_comparison": {
+      "Reference (16 kHz)": {
+        "bitrate_kbps": 256.0,
+        "objective_mos_lqo": 4.85,
+        "mcd_db": 0.0,
+        "lsd_db": 0.0,
+        "segmental_snr_db": 45.0,
+        "high_band_energy_ratio_percent": 3.641
+      },
+      "8-bit mu-law (128 kb/s)": {
+        "bitrate_kbps": 128.0,
+        "objective_mos_lqo": 4.738,
+        "mcd_db": 0.305,
+        "lsd_db": 0.641,
+        "segmental_snr_db": 37.484,
+        "high_band_energy_ratio_percent": 3.65
+      },
+      "WaveNet Waveform (WW)": {
+        "bitrate_kbps": 94.932,
+        "objective_mos_lqo": 4.738,
+        "mcd_db": 0.305,
+        "lsd_db": 0.641,
+        "segmental_snr_db": 37.484,
+        "high_band_energy_ratio_percent": 3.65
+      },
+      "AMR-WB (23.85 kb/s)": {
+        "bitrate_kbps": 23.85,
+        "objective_mos_lqo": 3.449,
+        "mcd_db": 3.197,
+        "lsd_db": 5.307,
+        "segmental_snr_db": 8.9,
+        "high_band_energy_ratio_percent": 3.397
+      },
+      "WaveNet Parametric (W_w, 2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 3.245,
+        "mcd_db": 3.42,
+        "lsd_db": 3.565,
+        "segmental_snr_db": 3.716,
+        "high_band_energy_ratio_percent": 3.671
+      },
+      "WaveNet Parametric (W_wo, 2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 2.23,
+        "mcd_db": 7.505,
+        "lsd_db": 7.905,
+        "segmental_snr_db": 1.537,
+        "high_band_energy_ratio_percent": 1.643
+      },
+      "MELP (2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 1.627,
+        "mcd_db": 8.412,
+        "lsd_db": 8.77,
+        "segmental_snr_db": -2.744,
+        "high_band_energy_ratio_percent": 0.041
+      },
+      "Codec 2 (2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 1.646,
+        "mcd_db": 8.277,
+        "lsd_db": 8.172,
+        "segmental_snr_db": -2.491,
+        "high_band_energy_ratio_percent": 0.023
+      },
+      "Speex (2.15 kb/s)": {
+        "bitrate_kbps": 2.15,
+        "objective_mos_lqo": 1.893,
+        "mcd_db": 7.459,
+        "lsd_db": 8.219,
+        "segmental_snr_db": 0.426,
+        "high_band_energy_ratio_percent": 0.069
+      }
+    },
+    "section_3_4_speaker_identification": {
+      "mulaw_speech_eer": {
+        "eer_percent": 12.053571428571429,
+        "eer_ci95_low_percent": 2.2209821428571432,
+        "eer_ci95_high_percent": 19.642857142857142,
+        "ci_95_low_percent": 2.2209821428571432,
+        "ci_95_high_percent": 19.642857142857142,
+        "threshold": 0.38931805399917674,
+        "mean_genuine_cosine": 0.5900887784230047,
+        "mean_impostor_cosine": 0.06415446283955947
+      },
+      "wavenet_parametric_w_wo_eer": {
+        "eer_percent": 25.0,
+        "eer_ci95_low_percent": 9.754464285714286,
+        "eer_ci95_high_percent": 37.957589285714285,
+        "ci_95_low_percent": 9.754464285714286,
+        "ci_95_high_percent": 37.957589285714285,
+        "threshold": 0.8321953156106268,
+        "mean_genuine_cosine": 0.8887528008846554,
+        "mean_impostor_cosine": 0.7299079903992016
+      },
+      "wavenet_parametric_w_w_eer": {
+        "eer_percent": 25.0,
+        "eer_ci95_low_percent": 5.357142857142857,
+        "eer_ci95_high_percent": 50.0,
+        "ci_95_low_percent": 5.357142857142857,
+        "ci_95_high_percent": 50.0,
+        "threshold": 0.47203084832451203,
+        "mean_genuine_cosine": 0.616406649899784,
+        "mean_impostor_cosine": 0.22985314070694823
+      },
+      "triangle_test_w_w_vs_w_wo": {
+        "mean_cosine_orig_vs_w_wo": 0.48838181445399975,
+        "mean_cosine_orig_vs_w_w": 0.8089489559047142,
+        "mean_cosine_w_wo_vs_w_w": 0.7808174503733418,
+        "mean_cosine_advantage": 0.3205671414507144,
+        "triangle_test_identification_percent": 43.24508910582853,
+        "triangle_accuracy_percent": 43.24508910582853,
+        "random_chance_baseline_percent": 33.33
+      }
+    }
+  },
+  "vctk_evaluation": {
+    "num_eval_utterances": 16,
+    "section_3_2_waveform_coding_rates": {
+      "mean_entropy_bits_per_sample": 5.7457,
+      "mean_entropy_kbps": 91.93,
+      "cross_entropy_bits_per_sample": 5.8275,
+      "cross_entropy_rate_kbps": 93.24,
+      "lossless_exact_match_rate": 1.0
+    },
+    "section_3_3_codec_comparison": {
+      "Reference (16 kHz)": {
+        "bitrate_kbps": 256.0,
+        "objective_mos_lqo": 4.85,
+        "mcd_db": 0.0,
+        "lsd_db": 0.0,
+        "segmental_snr_db": 45.0,
+        "high_band_energy_ratio_percent": 1.826
+      },
+      "8-bit mu-law (128 kb/s)": {
+        "bitrate_kbps": 128.0,
+        "objective_mos_lqo": 4.697,
+        "mcd_db": 0.501,
+        "lsd_db": 0.648,
+        "segmental_snr_db": 37.162,
+        "high_band_energy_ratio_percent": 1.834
+      },
+      "WaveNet Waveform (WW)": {
+        "bitrate_kbps": 93.24,
+        "objective_mos_lqo": 4.697,
+        "mcd_db": 0.501,
+        "lsd_db": 0.648,
+        "segmental_snr_db": 37.162,
+        "high_band_energy_ratio_percent": 1.834
+      },
+      "AMR-WB (23.85 kb/s)": {
+        "bitrate_kbps": 23.85,
+        "objective_mos_lqo": 3.364,
+        "mcd_db": 3.541,
+        "lsd_db": 5.359,
+        "segmental_snr_db": 8.749,
+        "high_band_energy_ratio_percent": 1.563
+      },
+      "WaveNet Parametric (W_w, 2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 3.11,
+        "mcd_db": 4.21,
+        "lsd_db": 4.056,
+        "segmental_snr_db": 4.352,
+        "high_band_energy_ratio_percent": 2.064
+      },
+      "WaveNet Parametric (W_wo, 2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 2.131,
+        "mcd_db": 8.193,
+        "lsd_db": 8.397,
+        "segmental_snr_db": 1.972,
+        "high_band_energy_ratio_percent": 1.51
+      },
+      "MELP (2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 1.454,
+        "mcd_db": 9.397,
+        "lsd_db": 9.09,
+        "segmental_snr_db": -2.683,
+        "high_band_energy_ratio_percent": 0.033
+      },
+      "Codec 2 (2.4 kb/s)": {
+        "bitrate_kbps": 2.4,
+        "objective_mos_lqo": 1.356,
+        "mcd_db": 10.187,
+        "lsd_db": 8.785,
+        "segmental_snr_db": -2.545,
+        "high_band_energy_ratio_percent": 0.015
+      },
+      "Speex (2.15 kb/s)": {
+        "bitrate_kbps": 2.15,
+        "objective_mos_lqo": 1.722,
+        "mcd_db": 8.392,
+        "lsd_db": 8.341,
+        "segmental_snr_db": 0.257,
+        "high_band_energy_ratio_percent": 0.038
+      }
+    },
+    "section_3_4_speaker_identification": {
+      "mulaw_speech_eer": {
+        "eer_percent": 25.0,
+        "eer_ci95_low_percent": 11.595982142857144,
+        "eer_ci95_high_percent": 42.44419642857144,
+        "ci_95_low_percent": 11.595982142857144,
+        "ci_95_high_percent": 42.44419642857144,
+        "threshold": 0.2121775659214813,
+        "mean_genuine_cosine": 0.3935697310774072,
+        "mean_impostor_cosine": 0.0036658837067926322
+      },
+      "wavenet_parametric_w_wo_eer": {
+        "eer_percent": 37.05357142857143,
+        "eer_ci95_low_percent": 20.044642857142858,
+        "eer_ci95_high_percent": 50.524553571428584,
+        "ci_95_low_percent": 20.044642857142858,
+        "ci_95_high_percent": 50.524553571428584,
+        "threshold": 0.8427541969049193,
+        "mean_genuine_cosine": 0.8848814072587434,
+        "mean_impostor_cosine": 0.8005241801782267
+      },
+      "wavenet_parametric_w_w_eer": {
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