Piper-TTS - Text-to-Speech Benchmark

Prerequisites

• Ubuntu 20.04 (x86_64)
• Git
• Python 3.10
• Picovoice Console Account

Usage

1. Clone the repository:

git clone https://github.com/Picovoice/text-to-speech-benchmark.git

2. Install the dependencies:

pip3 install -r requirements/requirements-piper-tts.txt

3. Download the picoLLM model

For each benchmark a picoLLM model is required to generate responses from the LLM. The picoLLM model used in the benchmark is llama-3.2-1b-instruct-385 and can be downloaded from Picovoice Console.

4. Run the benchmark:

Hugging face model download commit hash:

• Repo: rhasspy/piper-voices.
• Commit hash:
  • en_US-lessac-low.onnx: 217ddc79818708b078d0d14a8fae9608b9d77141.

For core hour ratio & latency metrics:

python3 benchmark.py \
--picovoice-access-key ${PV_ACCESS_KEY} \
--picollm-model-path ${PICOLLM_MODEL_PATH} \
--engine piper_tts \
--pipertts-model-path ${PIPER_MODEL_PATH} \

For peak memory metric:

for i in 1 2 3 4 5 6 7 8 9 10 20 40 80; do
    python3 benchmark.py \
    --picovoice-access-key ${PV_ACCESS_KEY} \
    --picollm-model-path ${PICOLLM_MODEL_PATH} \
    --engine piper_tts \
    --pipertts-model-path ${PIPER_MODEL_PATH} \
    --test-memory-size-multiple $i \
done

Replace ${PIPER_MODEL_PATH} with the path to Piper-TTS model. E.g. --pipertts-model-path ~/piper1-gpl/en_US-lessac-low.onnx. Replace ${PICOLLM_MODEL_PATH} with the path to the model you downloaded. Replace ${PV_ACCESS_KEY} with your AccessKey obtained from Picovoice Console. Everyone who signs up for Picovoice Console receives a unique AccessKey.