initial README.md

README.md

@@ -1 +1,120 @@
-Work in progress
+---
+language: "en"
+thumbnail:
+tags:
+- speechbrain
+- embeddings
+- Speaker
+- Identification
+- pytorch
+- ECAPA
+- TDNN
+license: "apache-2.0"
+datasets:
+- voxceleb
+---
+
+<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
+<br/><br/>
+
+# Mel-Spectrogram-based ECAPA-TDNN embeddings on Voxceleb
+### Note: This is a work in progress. This model is intended to be used with a Zero-Shot Multi-Speaker Text-to-Speech (TTS) model available [here](https://huggingface.co/speechbrain/tts-mstacotron2-libritts).
+
+This repository provides all the necessary tools to extract speaker embeddings using a pretrained ECAPA-TDNN model using SpeechBrain.
+Please note that this model is created for zero-shot multi-speaker TTS, and uses a mel-spectrogram as the input feature. It is trained on Voxceleb 1+ Voxceleb2 training data.
+
+## Pipeline description
+
+This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss.
+
+## Install SpeechBrain
+
+First of all, please install SpeechBrain with the following command:
+
+```
+pip install speechbrain
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Compute your speaker embeddings
+
+```python
+import torchaudio
+from speechbrain.pretrained import MelSpectrogramEncoder
+spk_emb_encoder = MelSpectrogramEncoder.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb-mel-spec")
+
+INPUT_SPEECH = "speech_sample.wav"
+ref_signal, signal_sr = torchaudio.load(INPUT_SPEECH)
+spk_embedding = spk_emb_encoder.encode_waveform(ref_signal)
+```
+The system is trained with recordings sampled at 16kHz (single channel).
+
+
+### Inference on GPU
+To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
+
+### Training
+The model was trained with SpeechBrain (aa018540).
+To train it from scratch follows these steps:
+1. Clone SpeechBrain:
+```bash
+git clone https://github.com/speechbrain/speechbrain/
+```
+2. Install it:
+```
+cd speechbrain
+pip install -r requirements.txt
+pip install -e .
+```
+
+3. Run Training:
+```
+cd  recipes/VoxCeleb/SpeakerRec
+python train_speaker_embeddings.py hparams/train_ecapa_tdnn_mel_spec.yaml --data_folder=your_data_folder --sample_rate=16000
+```
+
+The training logs will be available here in the future.
+
+
+### Limitations
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+
+#### Referencing ECAPA-TDNN
+```
+@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
+  author    = {Brecht Desplanques and
+               Jenthe Thienpondt and
+               Kris Demuynck},
+  editor    = {Helen Meng and
+               Bo Xu and
+               Thomas Fang Zheng},
+  title     = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
+               in {TDNN} Based Speaker Verification},
+  booktitle = {Interspeech 2020},
+  pages     = {3830--3834},
+  publisher = {{ISCA}},
+  year      = {2020},
+}
+```
+
+# **Citing SpeechBrain**
+Please, cite SpeechBrain if you use it for your research or business.
+
+```bibtex
+@misc{speechbrain,
+  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
+  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
+  year={2021},
+  eprint={2106.04624},
+  archivePrefix={arXiv},
+  primaryClass={eess.AS},
+  note={arXiv:2106.04624}
+}
+```
+
+# **About SpeechBrain**
+- Website: https://speechbrain.github.io/
+- Code: https://github.com/speechbrain/speechbrain/
+- HuggingFace: https://huggingface.co/speechbrain/