Text-to-Speech
Transformers
Safetensors
GGUF
llama
text-generation
speech-synthesis
multilingual
indic
orpheus
lora
low-latency
zero-shot
emotions
discrete-audio-tokens
text-generation-inference
Instructions to use kenpath/svara-tts-voiceclone-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenpath/svara-tts-voiceclone-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="kenpath/svara-tts-voiceclone-beta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kenpath/svara-tts-voiceclone-beta") model = AutoModelForCausalLM.from_pretrained("kenpath/svara-tts-voiceclone-beta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from kenpath/svara-tts-voiceclone-beta: direct link, hf CLI and curl.
- Browser
- Download file 22.8 MB
-
https://huggingface.co/kenpath/svara-tts-voiceclone-beta/resolve/main/tokenizer.json
- Command line
-
hf download hf://kenpath/svara-tts-voiceclone-beta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/kenpath/svara-tts-voiceclone-beta/resolve/main/tokenizer.json
22.8 MB
- Xet hash:
- 943eac2190bc841b30bc637b3c40b86087d790464f6a459cdb3cabe8b76443ef
- Size of remote file:
- 22.8 MB
- SHA256:
- 044e2a10201774018db120391980464472baabf223bd353cea49b17da0b66abc
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