Update README.md

README.md

@@ -44,6 +44,8 @@ fs.writeFileSync('result.wav', wav.toBuffer());
 
 **Example:** Load processor, tokenizer, and models separately.
 ```js
+import { AutoTokenizer, AutoProcessor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, Tensor } from '@xenova/transformers';
+
 // Load the tokenizer and processor
 const tokenizer = await AutoTokenizer.from_pretrained('Xenova/speecht5_tts');
 const processor = await AutoProcessor.from_pretrained('Xenova/speecht5_tts');
@@ -76,6 +78,19 @@ console.log(waveform)
 //   data: Float32Array(26112) [ -0.00043630177970044315, -0.00018082228780258447, ... ],
 // }
 ```
+
+Optionally, save the audio to a wav file (Node.js):
+```js
+// Write to file (Node.js)
+import wavefile from 'wavefile';
+import fs from 'fs';
+
+const wav = new wavefile.WaveFile();
+wav.fromScratch(1, processor.feature_extractor.config.sampling_rate, '32f', waveform.data);
+fs.writeFileSync('out.wav', wav.toBuffer());
+```
+
+
 ---
 
 Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).