Update README.md

README.md

@@ -35,6 +35,7 @@ Please notice that we encourage you to read our tutorials and learn more about
 
 ### Using the Vocoder
 
+- *Basic Usage:**
 ```python
 import torch
 from speechbrain.pretrained import HIFIGAN
@@ -45,6 +46,56 @@ mel_specs = torch.rand(2, 80,298)
 waveforms = hifi_gan.decode_batch(mel_specs)
 ```
 
+- *Spectrogram to Waveform Conversion:**
+- 
+```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-22050Hz", savedir="tmpdir_voc22050")
+
+# Load an audio file (an example file can be found in this repository)
+# Ensure that the audio signal is sampled at 22050 Hz; refer to the provided link for a 16000 Hz Vocoder.
+#signal, rate = torchaudio.load('speechbrain/tts-hifigan-libritts-22050H/example_22kHz.wav')
+signal, rate = torchaudio.load('/home/mirco/Downloads/example_22kHz.wav')
+
+# Ensure the audio is sigle channel
+signal = signal[0].squeeze()
+
+torchaudio.save('waveform.wav', signal.unsqueeze(0), 22050)
+
+# 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=22050,
+    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), 22050)
+
+# If everything is set up correctly, the original and reconstructed audio should be nearly indistinguishable.
+
+```
+
+
 ### Using the Vocoder with the TTS
 ```python
 import torchaudio