Update README.md

README.md

@@ -46,6 +46,53 @@ mel_specs = torch.rand(2, 80,298)
 waveforms = hifi_gan.decode_batch(mel_specs)
 ```
 
+```python
+import torchaudio
+from speechbrain.pretrained import HIFIGAN
+from speechbrain.lobes.models.FastSpeech2 import mel_spectogram
+
+# Load a pretrained HIFIGAN Vocoder
+hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-libritts-16kHz", savedir="tmpdir")
+
+# Load an audio file (an example file can be found in this repository)
+# Ensure that the audio signal is sampled at 16000 Hz; refer to the provided link for a 22050 Hz Vocoder.
+#signal, rate = torchaudio.load('speechbrain/tts-hifigan-libritts-16kHz/example_16kHz.wav')
+signal, rate = torchaudio.load('/home/mirco/Downloads/example_16kHz.wav')
+
+# Ensure the audio is sigle channel
+signal = signal[0].squeeze()
+
+torchaudio.save('waveform.wav', signal.unsqueeze(0), 16000)
+
+# Compute the mel spectrogram.
+# IMPORTANT: Use these specific parameters to match the Vocoder's training settings for optimal results.
+spectrogram, _ = mel_spectogram(
+    audio=signal.squeeze(),
+    sample_rate=16000,
+    hop_length=256,
+    win_length=1024,
+    n_mels=80,
+    n_fft=1024,
+    f_min=0.0,
+    f_max=8000.0,
+    power=1,
+    normalized=False,
+    min_max_energy_norm=True,
+    norm="slaney",
+    mel_scale="slaney",
+    compression=True
+)
+
+# Convert the spectrogram to waveform
+waveforms = hifi_gan.decode_batch(spectrogram)
+
+# Save the reconstructed audio as a waveform
+torchaudio.save('waveform_reconstructed.wav', waveforms.squeeze(1), 16000)
+
+# If everything is set up correctly, the original and reconstructed audio should be nearly indistinguishable.
+
+```
+
 ### Inference on GPU
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.