update README.md

Author: yistLin

README.md

@@ -5,10 +5,6 @@ The original paper about GE2E loss could be found here: [Generalized End-to-End
 
 ## Usage
 
-You can download the pretrained models from: [Wiki - Pretrained Models](https://github.com/yistLin/dvector/wiki/Pretrained-Models).
-
-Since the models are compiled with TorchScript, you can simply load and use a pretrained d-vector anywhere.
-
 ```python
 import torch
 import torchaudio
@@ -17,10 +13,28 @@ wav2mel = torch.jit.load("wav2mel.pt")
 dvector = torch.jit.load("dvector.pt").eval()
 
 wav_tensor, sample_rate = torchaudio.load("example.wav")
-mel_tensor = wav2mel(wav_tensor, sample_rate)
-emb_tensor = dvector.embed_utterance(mel_tensor)
+mel_tensor = wav2mel(wav_tensor, sample_rate)  # shape: (frames, mel_dim)
+emb_tensor = dvector.embed_utterance(mel_tensor)  # shape: (emb_dim)
+```
+
+You can also embed multiple utterances of a speaker at once:
+
+```python
+emb_tensor = dvector.embed_utterances([mel_tensor_1, mel_tensor_2])  # shape: (emb_dim)
 ```
 
+There are 2 modules in this example:
+- `wav2mel.pt` is the preprocessing module which is composed of 2 modules:
+    - `sox_effects.pt` is used to normalize volume, remove silence, resample audio to 16 KHz, 16 bits, and remix all channels to single channel
+    - `log_melspectrogram.pt` is used to transform waveforms to log mel spectrograms
+- `dvector.pt` is the speaker encoder
+
+Since all the modules are compiled with [TorchScript](https://pytorch.org/docs/stable/jit.html), you can simply load them and use anywhere **without any dependencies**.
+
+### Pretrianed models & preprocessing modules
+
+You can download them from the page of [*Releases*](https://github.com/yistLin/dvector/releases).
+
 ## Train from scratch
 
 ### Preprocess training data
@@ -38,12 +52,12 @@ python preprocess.py VoxCeleb1/dev LibriSpeech/train-clean-360 -o preprocessed
 ```
 
 If you need to modify some audio preprocessing hyperparameters, directly modify `data/wav2mel.py`.
-After preprocessing, 3 modules will be saved in the output directory:
+After preprocessing, 3 preprocessing modules will be saved in the output directory:
 1. `wav2mel.pt`
 2. `sox_effects.pt`
 3. `log_melspectrogram.pt`
 
-> The first module `wav2mel.pt` is actually composed of the second and the third modules.
+> The first module `wav2mel.pt` is composed of the second and the third modules.
 > These modules were compiled with TorchScript and can be used anywhere to preprocess audio data.
 
 ### Train a model
@@ -57,7 +71,12 @@ python train.py preprocessed <model_dir>
 During training, logs will be put under `<model_dir>/logs` and checkpoints will be placed under `<model_dir>/checkpoints`.
 For more details, check the usage with `python train.py -h`.
 
-### Visualize speaker embeddings
+### Use different speaker encoders
+
+By default I'm using 3-layerd LSTM with attentive pooling as the speaker encoder, but you can use speaker encoders of different architecture.
+For more information, please take a look at `modules/dvector.py`.
+
+## Visualize speaker embeddings
 
 You can visualize speaker embeddings using a trained d-vector.
 Note that you have to structure speakers' directories in the same way as for preprocessing.