Add model weights and configuration files

.gitattributes

@@ -33,3 +33,4 @@ 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
+*.wav filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,145 @@
----
-license: mit
----
+---
+license: apache-2.0
+library_name: transformers.js
+language:
+- en
+base_model:
+- hexgrad/Kokoro-82M
+pipeline_tag: text-to-speech
+---
+
+# Kokoro TTS
+
+Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out).
+
+## Table of contents
+
+- [Usage](#usage)
+  - [JavaScript](#javascript)
+  - [Python](#python)
+- [Voices/Samples](#voicessamples)
+- [Quantizations](#quantizations)
+
+
+## Usage
+
+### JavaScript
+
+First, install the `kokoro-js` library from [NPM](https://npmjs.com/package/kokoro-js) using:
+```bash
+npm i kokoro-js
+```
+
+You can then generate speech as follows:
+
+```js
+import { KokoroTTS } from "kokoro-js";
+
+const model_id = "onnx-community/Kokoro-82M-ONNX";
+const tts = await KokoroTTS.from_pretrained(model_id, {
+  dtype: "q8", // Options: "fp32", "fp16", "q8", "q4", "q4f16"
+});
+
+const text = "Life is like a box of chocolates. You never know what you're gonna get.";
+const audio = await tts.generate(text, {
+  // Use `tts.list_voices()` to list all available voices
+  voice: "af_bella",
+});
+audio.save("audio.wav");
+```
+
+
+### Python
+
+```python
+import os
+import numpy as np
+from onnxruntime import InferenceSession
+
+# You can generate token ids as follows:
+#   1. Convert input text to phonemes using https://github.com/hexgrad/misaki
+#   2. Map phonemes to ids using https://huggingface.co/hexgrad/Kokoro-82M/blob/785407d1adfa7ae8fbef8ffd85f34ca127da3039/config.json#L34-L148
+tokens = [50, 157, 43, 135, 16, 53, 135, 46, 16, 43, 102, 16, 56, 156, 57, 135, 6, 16, 102, 62, 61, 16, 70, 56, 16, 138, 56, 156, 72, 56, 61, 85, 123, 83, 44, 83, 54, 16, 53, 65, 156, 86, 61, 62, 131, 83, 56, 4, 16, 54, 156, 43, 102, 53, 16, 156, 72, 61, 53, 102, 112, 16, 70, 56, 16, 138, 56, 44, 156, 76, 158, 123, 56, 16, 62, 131, 156, 43, 102, 54, 46, 16, 102, 48, 16, 81, 47, 102, 54, 16, 54, 156, 51, 158, 46, 16, 70, 16, 92, 156, 135, 46, 16, 54, 156, 43, 102, 48, 4, 16, 81, 47, 102, 16, 50, 156, 72, 64, 83, 56, 62, 16, 156, 51, 158, 64, 83, 56, 16, 44, 157, 102, 56, 16, 44, 156, 76, 158, 123, 56, 4]
+
+# Context length is 512, but leave room for the pad token 0 at the start & end
+assert len(tokens) <= 510, len(tokens)
+
+# Style vector based on len(tokens), ref_s has shape (1, 256)
+voices = np.fromfile('./voices/af.bin', dtype=np.float32).reshape(-1, 1, 256)
+ref_s = voices[len(tokens)]
+
+# Add the pad ids, and reshape tokens, should now have shape (1, <=512)
+tokens = [[0, *tokens, 0]]
+
+model_name = 'model.onnx' # Options: model.onnx, model_fp16.onnx, model_quantized.onnx, model_q8f16.onnx, model_uint8.onnx, model_uint8f16.onnx, model_q4.onnx, model_q4f16.onnx
+sess = InferenceSession(os.path.join('onnx', model_name))
+
+audio = sess.run(None, dict(
+    input_ids=tokens,
+    style=ref_s,
+    speed=np.ones(1, dtype=np.float32),
+))[0]
+```
+
+Optionally, save the audio to a file:
+```py
+import scipy.io.wavfile as wavfile
+wavfile.write('audio.wav', 24000, audio[0])
+```
+
+
+## Voices/Samples
+
+
+> Life is like a box of chocolates. You never know what you're gonna get.
+
+
+| Name         | Nationality | Gender | Sample                                                                                                                                  |
+| ------------ | ----------- | ------ | --------------------------------------------------------------------------------------------------------------------------------------- |
+| **af_heart** | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/S_9tkA75BT_QHKOzSX6S-.wav"></audio> |
+| af_alloy     | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/wiZ3gvlL--p5pRItO4YRE.wav"></audio> |
+| af_aoede     | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/Nv1xMwzjTdF9MR8v0oEEJ.wav"></audio> |
+| af_bella     | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/sWN0rnKU6TlLsVdGqRktF.wav"></audio> |
+| af_jessica   | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/2Oa4wITWAmiCXJ_Q97-7R.wav"></audio> |
+| af_kore      | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/AOIgyspzZWDGpn7oQgwtu.wav"></audio> |
+| af_nicole    | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/EY_V2OGr-hzmtTGrTCTyf.wav"></audio> |
+| af_nova      | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/X-xdEkx3GPlQG5DK8Gsqd.wav"></audio> |
+| af_river     | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/ZqaV2-xGUZdBQmZAF1Xqy.wav"></audio> |
+| af_sarah     | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/xzoJBl1HCvkE8Fl8Xu2R4.wav"></audio> |
+| af_sky       | American    | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/ubebYQoaseyQk-jDLeWX7.wav"></audio> |
+| am_adam      | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/tvauhDVRGvGK98I-4wv3H.wav"></audio> |
+| am_echo      | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/qy_KuUB0hXsu-u8XaJJ_Z.wav"></audio> |
+| am_eric      | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/JhqPjbpMhraUv5nTSPpwD.wav"></audio> |
+| am_fenrir    | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/c0R9caBdBiNjGUUalI_DQ.wav"></audio> |
+| am_liam      | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/DFHvulaLeOjXIDKecvNG3.wav"></audio> |
+| am_michael   | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/IPKhsnjq1tPh3JmHH8nEg.wav"></audio> |
+| am_onyx      | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/ov0pFDfE8NNKZ80LqW6Di.wav"></audio> |
+| am_puck      | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/MOC654sLMHWI64g8HWesV.wav"></audio> |
+| am_santa     | American    | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/LzA6JmHBvQlhOviy8qVfJ.wav"></audio> |
+| bf_alice    | British     | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/9mnYZ3JWq7f6U12plXilA.wav"></audio> |
+| bf_emma     | British     | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/_fvGtKMttRI0cZVGqxMh8.wav"></audio> |
+| bf_isabella | British     | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/VzlcJpqGEND_Q3duYnhiu.wav"></audio> |
+| bf_lily     | British     | Female | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/qZCoartohiRlVamY8Xpok.wav"></audio> |
+| bm_daniel   | British     | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/Eb0TLnLXHDRYOA3TJQKq3.wav"></audio> |
+| bm_fable    | British     | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/NT9XkmvlezQ0FJ6Th5hoZ.wav"></audio> |
+| bm_george   | British     | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/y6VJbCESszLZGupPoqNkF.wav"></audio> |
+| bm_lewis    | British     | Male   | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/RlB5BRvLt-IFvTjzQNxCh.wav"></audio> |
+
+
+## Quantizations
+
+The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size. 
+
+> How could I know? It's an unanswerable question. Like asking an unborn child if they'll lead a good life. They haven't even been born.
+
+
+| Model                                          | Size (MB) | Sample                                                                                                                                  |
+|------------------------------------------------|-----------|-----------------------------------------------------------------------------------------------------------------------------------------|
+| model.onnx (fp32)                              | 326       | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/njexBuqPzfYUvWgs9eQ-_.wav"></audio> |
+| model_fp16.onnx (fp16)                         | 163       | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/8Ebl44hMQonZs4MlykExt.wav"></audio> |
+| model_quantized.onnx (8-bit)                   | 92.4      | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/9SLOt6ETclZ4yRdlJ0VIj.wav"></audio> |
+| model_q8f16.onnx (Mixed precision)             | 86        | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/gNDMqb33YEmYMbAIv_Grx.wav"></audio> |
+| model_uint8.onnx (8-bit & mixed precision)     | 177       | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/tpOWRHIWwEb0PJX46dCWQ.wav"></audio> |
+| model_uint8f16.onnx (Mixed precision)          | 114       | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/vtZhABzjP0pvGD7dRb5Vr.wav"></audio> |
+| model_q4.onnx (4-bit matmul)                   | 305       | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/8FVn0IJIUfccEBWq8Fnw_.wav"></audio> |
+| model_q4f16.onnx (4-bit matmul & fp16 weights) | 154       | <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/7DrgWC_1q00s-wUJuG44X.wav"></audio> |

audio.wav

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+size 374458

config.json

@@ -0,0 +1,3 @@
+{
+  "model_type": "style_text_to_speech_2"
+}

config_kokoro.json

@@ -0,0 +1,150 @@
+{
+  "istftnet": {
+    "upsample_kernel_sizes": [20, 12],
+    "upsample_rates": [10, 6],
+    "gen_istft_hop_size": 5,
+    "gen_istft_n_fft": 20,
+    "resblock_dilation_sizes": [
+      [1, 3, 5],
+      [1, 3, 5],
+      [1, 3, 5]
+    ],
+    "resblock_kernel_sizes": [3, 7, 11],
+    "upsample_initial_channel": 512
+  },
+  "dim_in": 64,
+  "dropout": 0.2,
+  "hidden_dim": 512,
+  "max_conv_dim": 512,
+  "max_dur": 50,
+  "multispeaker": true,
+  "n_layer": 3,
+  "n_mels": 80,
+  "n_token": 178,
+  "style_dim": 128,
+  "text_encoder_kernel_size": 5,
+  "plbert": {
+    "hidden_size": 768,
+    "num_attention_heads": 12,
+    "intermediate_size": 2048,
+    "max_position_embeddings": 512,
+    "num_hidden_layers": 12,
+    "dropout": 0.1
+  },
+  "vocab": {
+    ";": 1,
+    ":": 2,
+    ",": 3,
+    ".": 4,
+    "!": 5,
+    "?": 6,
+    "—": 9,
+    "…": 10,
+    "\"": 11,
+    "(": 12,
+    ")": 13,
+    "“": 14,
+    "”": 15,
+    " ": 16,
+    "\u0303": 17,
+    "ʣ": 18,
+    "ʥ": 19,
+    "ʦ": 20,
+    "ʨ": 21,
+    "ᵝ": 22,
+    "\uAB67": 23,
+    "A": 24,
+    "I": 25,
+    "O": 31,
+    "Q": 33,
+    "S": 35,
+    "T": 36,
+    "W": 39,
+    "Y": 41,
+    "ᵊ": 42,
+    "a": 43,
+    "b": 44,
+    "c": 45,
+    "d": 46,
+    "e": 47,
+    "f": 48,
+    "h": 50,
+    "i": 51,
+    "j": 52,
+    "k": 53,
+    "l": 54,
+    "m": 55,
+    "n": 56,
+    "o": 57,
+    "p": 58,
+    "q": 59,
+    "r": 60,
+    "s": 61,
+    "t": 62,
+    "u": 63,
+    "v": 64,
+    "w": 65,
+    "x": 66,
+    "y": 67,
+    "z": 68,
+    "ɑ": 69,
+    "ɐ": 70,
+    "ɒ": 71,
+    "æ": 72,
+    "β": 75,
+    "ɔ": 76,
+    "ɕ": 77,
+    "ç": 78,
+    "ɖ": 80,
+    "ð": 81,
+    "ʤ": 82,
+    "ə": 83,
+    "ɚ": 85,
+    "ɛ": 86,
+    "ɜ": 87,
+    "ɟ": 90,
+    "ɡ": 92,
+    "ɥ": 99,
+    "ɨ": 101,
+    "ɪ": 102,
+    "ʝ": 103,
+    "ɯ": 110,
+    "ɰ": 111,
+    "ŋ": 112,
+    "ɳ": 113,
+    "ɲ": 114,
+    "ɴ": 115,
+    "ø": 116,
+    "ɸ": 118,
+    "θ": 119,
+    "œ": 120,
+    "ɹ": 123,
+    "ɾ": 125,
+    "ɻ": 126,
+    "ʁ": 128,
+    "ɽ": 129,
+    "ʂ": 130,
+    "ʃ": 131,
+    "ʈ": 132,
+    "ʧ": 133,
+    "ʊ": 135,
+    "ʋ": 136,
+    "ʌ": 138,
+    "ɣ": 139,
+    "ɤ": 140,
+    "χ": 142,
+    "ʎ": 143,
+    "ʒ": 147,
+    "ʔ": 148,
+    "ˈ": 156,
+    "ˌ": 157,
+    "ː": 158,
+    "ʰ": 162,
+    "ʲ": 164,
+    "↓": 169,
+    "→": 171,
+    "↗": 172,
+    "↘": 173,
+    "ᵻ": 177
+  }
+}

inference.py

@@ -0,0 +1,53 @@
+import os
+import json
+import numpy as np
+import scipy.io.wavfile as wavfile
+from onnxruntime import InferenceSession
+from phonemizer import phonemize
+
+# === Step 1: Load phoneme-to-ID vocabulary ===
+CONFIG_PATH = "./config_kokoro.json"  # Download this from Hugging Face: Kokoro-82M/config.json
+with open(CONFIG_PATH, "r", encoding="utf-8") as f:
+    config = json.load(f)
+phoneme_to_id = config["vocab"]
+
+# === Step 2: Convert text to phonemes using espeak-ng ===
+text = "Hi how are you, what is your name. tell me something"
+
+phonemes = phonemize(
+    text,
+    language="en-us",
+    backend="espeak",
+    strip=True,
+    preserve_punctuation=True,
+    with_stress=True
+)
+
+# === Step 3: Filter out unsupported phonemes and convert to token IDs ===
+phonemes = "".join(p for p in phonemes if p in phoneme_to_id)
+print("Phonemes:", phonemes)
+
+tokens = [phoneme_to_id[p] for p in phonemes]
+print("Token IDs:", tokens)
+
+# === Step 4: Prepare style embedding and input IDs ===
+assert len(tokens) <= 510, "Token sequence too long (max 510 phonemes)"
+
+voices = np.fromfile('./voices/af.bin', dtype=np.float32).reshape(-1, 1, 256)
+ref_s = voices[len(tokens)]  # Select style vector based on token length
+
+tokens = [[0, *tokens, 0]]  # Add padding tokens at the beginning and end
+
+# === Step 5: Run ONNX model inference ===
+model_name = 'model.onnx'
+sess = InferenceSession(os.path.join('onnx', model_name))
+
+audio = sess.run(None, {
+    'input_ids': tokens,
+    'style': ref_s,
+    'speed': np.ones(1, dtype=np.float32),
+})[0]
+
+# === Step 6: Save output audio as a 24kHz WAV file ===
+wavfile.write('audio.wav', 24000, audio[0])
+print("✅ Audio saved to audio.wav")

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requirement.txt

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+phonemizer==3.2.1
+espeakng==1.0.1
+numpy>=1.21
+onnxruntime>=1.16.0
+scipy>=1.7

tokenizer.json

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+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [],
+  "normalizer": {
+    "type": "Replace",
+    "pattern": {
+      "Regex": "[^$;:,.!?\u2014\u2026\"()\u201c\u201d \u0303\u02a3\u02a5\u02a6\u02a8\u1d5d\uab67AIOQSTWY\u1d4aabcdefhijklmnopqrstuvwxyz\u0251\u0250\u0252\u00e6\u03b2\u0254\u0255\u00e7\u0256\u00f0\u02a4\u0259\u025a\u025b\u025c\u025f\u0261\u0265\u0268\u026a\u029d\u026f\u0270\u014b\u0273\u0272\u0274\u00f8\u0278\u03b8\u0153\u0279\u027e\u027b\u0281\u027d\u0282\u0283\u0288\u02a7\u028a\u028b\u028c\u0263\u0264\u03c7\u028e\u0292\u0294\u02c8\u02cc\u02d0\u02b0\u02b2\u2193\u2192\u2197\u2198\u1d7b]"
+    },
+    "content": ""
+  },
+  "pre_tokenizer": {
+    "type": "Split",
+    "pattern": {
+      "Regex": ""
+    },
+    "behavior": "Isolated",
+    "invert": false
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