neutts-air/examples

Examples

GGUF Backbones

To run the model with llama-cpp-python in GGUF format, select a GGUF backbone when intializing the example script.

python -m examples.basic_example \
  --input_text "My name is Dave, and um, I'm from London" \
  --ref_audio ./samples/dave.wav \
  --ref_text ./samples/dave.txt \
  --backbone neuphonic/neutts-air-q4-gguf

Pre-encode a reference

Reference encoding can be done ahead of time to reduce latency whilst inferencing the model; to pre-encode a reference you only need to provide a reference audio, as in the following script:

python -m examples.encode_reference \
 --ref_audio  ./samples/dave.wav \
 --output_path encoded_reference.pt

Minimal Latency Example

To take advantage of encoding references ahead of time, we have a compiled the codec decoder into an onnx graph that enables inferencing NeuTTS-Air without loading the encoder. This can be useful for running the model in resource-constrained environments where the encoder may add a large amount of extra latency/memory usage.

To test the decoder, make sure you have installed onnxruntime and run the following:

python -m examples.onnx_example \
  --input_text "My name is Dave, and um, I'm from London" \
  --ref_codes samples/dave.pt \
  --ref_text samples/dave.txt \
  --backbone neuphonic/neutts-air-q4-gguf

Streaming Support

To stream the model output in chunks, try out the onnx_streaming.py example. For streaming, only the GGUF backends are currently supported. Ensure you have llama-cpp-pyhon, onnxruntime and pyaudio installed to run this example.

python -m examples.basic_streaming_example \
  --input_text "My name is Dave, and um, I'm from London" \
  --ref_codes samples/dave.pt \
  --ref_text samples/dave.txt \
  --backbone neuphonic/neutts-air-q4-gguf