Instructions to use Cintin/parler-tts-mini-Jenny-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cintin/parler-tts-mini-Jenny-colab with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Cintin/parler-tts-mini-Jenny-colab")

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("Cintin/parler-tts-mini-Jenny-colab", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Cintin/parler-tts-mini-Jenny-colab with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cintin/parler-tts-mini-Jenny-colab"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cintin/parler-tts-mini-Jenny-colab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Cintin/parler-tts-mini-Jenny-colab

SGLang

How to use Cintin/parler-tts-mini-Jenny-colab with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Cintin/parler-tts-mini-Jenny-colab" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cintin/parler-tts-mini-Jenny-colab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Cintin/parler-tts-mini-Jenny-colab" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cintin/parler-tts-mini-Jenny-colab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Cintin/parler-tts-mini-Jenny-colab with Docker Model Runner:
```
docker model run hf.co/Cintin/parler-tts-mini-Jenny-colab
```

How to Get Started with the Model

Use the code below to get started with the model.

!pip install git+https://github.com/huggingface/parler-tts.git

Quick Start

from parler_tts import ParlerTTSForConditionalGeneration
from transformers import AutoTokenizer
import torch

device = "cuda:0" if torch.cuda.is_available() else "cpu"

# model = ParlerTTSForConditionalGeneration.from_pretrained("/kaggle/working/parler-tts/output_dir_training", torch_dtype=torch.float16).to(device)
# tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1")

model = ParlerTTSForConditionalGeneration.from_pretrained("Cintin/parler-tts-mini-Jenny-colab").to(device)
tokenizer = AutoTokenizer.from_pretrained("Cintin/parler-tts-mini-Jenny-colab")

prompt = "Hey, how are you doing today?"
description = "'Jenny delivers her words quite expressively, in a very confined sounding environment with clear audio quality. She speaks fast.'"

input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
audio_arr = generation.cpu().numpy().squeeze()

To play the audio

from IPython.display import Audio
Audio(audio_arr, rate=model.config.sampling_rate)

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Safetensors

Model size

0.6B params

Tensor type

F16