Update README.md

README.md

@@ -1,3 +1,49 @@
 ---
 license: cc-by-4.0
+datasets:
+- cdminix/libritts-aligned
+language:
+- en
+tags:
+- speech recognition, speech synthesis, text-to-speech
 ---
+
+This model requires the Vocex library, which is available using
+```pip install vocex```
+
+
+Vocex extracts several measures (as well as d-vectors) from audio.
+![summary](https://raw.githubusercontent.com/MiniXC/vocex/main/demo/summary.png)
+You can read more here:
+https://github.com/minixc/vocex
+
+## Usage
+```python
+from vocex import Vocex
+import torchaudio # or any other audio loading library
+
+model = vocex.from_checkpoint('vocex/cdminix') # an fp16 model is loaded by default
+model = vocex.from_checkpoint('vocex/cdminix', fp16=False) # to load a fp32 model
+model = vocex.from_checkpoint('some/path/model.ckpt') # to load local checkpoint
+
+audio = ... # a numpy or torch array is required with shape [batch_size, length_in_samples] or just [length_in_samples]
+sample_rate = ... # we need to specify a sample rate if the audio is not sampled at 22050
+
+outputs = model(audio, sample_rate)
+pitch, energy, snr, srmr = (
+    outputs["measures"]["pitch"], 
+    outputs["measures"]["energy"],
+    outputs["measures"]["snr"],
+    outputs["measures"]["srmr"],
+)
+d_vector = outputs["d_vector"] # a torch tensor with shape [batch_size, 256]
+
+# you can also get activations and attention weights at all layers of the model
+outputs = model(audio, sample_rate, return_activations=True, return_attention=True)
+activations = outputs["activations"] # a list of torch tensors with shape [batch_size, layers, ...]
+attention = outputs["attention"] # a list of torch tensors with shape [batch_size, layers, ...]
+
+# there are also speaker avatars, which are a 2D representation of the speaker's voice
+outputs = model(audio, sample_rate, return_avatar=True)
+avatar = outputs["avatars"] # a torch tensor with shape [batch_size, 256, 256]
+```