metadata

license: apache-2.0
task_categories:
  - text-to-speech
language:
  - en
tags:
  - kokoro-82M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: voices
      dtype: string
    - name: tensor
      sequence:
        sequence:
          sequence: float64
  splits:
    - name: train
      num_bytes: 11556949
      num_examples: 11
  download_size: 6344291
  dataset_size: 11556949

Kokoro-82M Voices

This dataset contains all the voices available in hexgrad/Kokoro-82M. This dataset provides the voices in 3 different formats.

Individual voices embeddings in different JSON file
Single JSON which contains all the voices in a JSON object.
Parquet format for usage via datasets The voices name is the same as the .pth file names shown below.

voices = [
    "af",
    "af_bella",
    "af_nicole",
    "af_sarah",
    "af_sky",
    "am_adam",
    "am_michael",
    "bf_emma",
    "bf_isabella",
    "bm_george",
    "bm_lewis",
]

For example, the "af" voices embeddings can be obtained using the "af.json" file which contains the embeddings as a JSON array. The same "af" voices can be obtain in the single file by accessing the "af" name in the JSON object. See Usage for more information on how to retreive the embeddings.

Usage

Some example usage of downloading the "af" voice embedding.

Downloading the JSON All

# pip install -U -q huggingface_hub numpy
import json

import numpy as np
from huggingface_hub import hf_hub_download

file = hf_hub_download(
    repo_id="ecyht2/kokoro-82M-voices",
    repo_type="dataset",
    filename="voices.json"
)
with open(file, "r", encoding="utf-8") as f:
    voices = json.load(f)
    voice = np.array(voices.get("af")) # Change "af" to the voice that you want
print(voice.shape)
# (511, 1, 256)

Downloading the JSON Single

# pip install -U -q huggingface_hub numpy
import json

import numpy as np
from huggingface_hub import hf_hub_download

file = hf_hub_download(
    repo_id="ecyht2/kokoro-82M-voices",
    repo_type="dataset",
    filename="af.json" # Change "af" to the voice that you want
)
with open(file, "r", encoding="utf-8") as f:
    voice = json.load(f)
    voice = np.array(voice)
print(voice.shape)
# (511, 1, 256)

Using Huggingface Datasets

# pip install -U -q datasets numpy
from datasets import load_dataset
ds = load_dataset(repo, split="train")

for i in ds:
    if i["voices"] == "af":
        voice = np.array(i["tensor"])
        break
print(voice.shape)
# (511, 1, 256)