Add code example

README.md

@@ -19,13 +19,51 @@ license: apache-2.0
 txtai has a built in Text to Speech (TTS) pipeline that makes using this model easy.
 
 ```python
+import soundfile as sf
+
+from txtai.pipeline import TextToSpeech
+
+# Build pipeline
+tts = TextToSpeech("NeuML/ljspeech-vits-onnx")
+
+# Generate speech
+speech = tts("Say something here")
+
+# Write to file
+sf.write("out.wav", speech, 22050)
 ```
 
 ## Usage with ONNX
 
 This model can also be run directly with ONNX provided the input text is tokenized. Tokenization can be done with [ttstokenizer](https://github.com/neuml/ttstokenizer).
 
+Note that the txtai pipeline has additional functionality such as batching large inputs together that would need to be duplicated with this method.
+
 ```python
+import onnxruntime
+import soundfile as sf
+import yaml
+
+from ttstokenizer import TTSTokenizer
+
+# This example assumes the files have been downloaded locally
+with open("ljspeech-vits-onnx/config.yaml", "r", encoding="utf-8") as f:
+    config = yaml.safe_load(f)
+
+# Create model
+model = onnxruntime.InferenceSession("ljspeech-vits-onnx/model.onnx", providers=["CPUExecutionProvider"])
+
+# Create tokenizer
+tokenizer = TTSTokenizer(config["token"]["list"])
+
+# Tokenize inputs
+inputs = tokenizer("Say something here")
+
+# Generate speech
+outputs = model.run(None, {"text": inputs})
+
+# Write to file
+sf.write("out.wav", outputs[0], 22050)
 ```
 
 ## How to export