Update README.md

README.md

@@ -71,7 +71,6 @@ sf.write("speech.wav", waveform.squeeze().detach().numpy(), samplerate=22050)
 3. Run inference via the Transformers modelling code with the model and hifigan combined
 
 ```python
-
 from transformers import FastSpeech2ConformerTokenizer, FastSpeech2ConformerWithHifiGan
 import soundfile as sf
 
@@ -86,6 +85,19 @@ waveform = output_dict["waveform"]
 sf.write("speech.wav", waveform.squeeze().detach().numpy(), samplerate=22050)
 ```
 
+4. Run inference with a pipeline and specify which vocoder to use
+```python
+from transformers import pipeline, FastSpeech2ConformerHifiGan
+import soundfile as sf
+
+vocoder = FastSpeech2ConformerHifiGan.from_pretrained("espnet/fastspeech2_conformer_hifigan")
+synthesiser = pipeline(model="espnet/fastspeech2_conformer", vocoder=vocoder)
+
+speech = synthesiser("Hello, my dog is cooler than you!")
+
+sf.write("speech.wav", speech["audio"].squeeze(), samplerate=speech["sampling_rate"])
+```
+
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->