upload model

.gitattributes

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 *.pb filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
+classifier.ckpt filter=lfs diff=lfs merge=lfs -text
+embedding_model.ckpt filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language: "en"
+thumbnail:
+tags:
+- speechbrain
+- embeddings
+- Sound
+- Keywords
+- Keyword Spotting
+- pytorch
+- ECAPA-TDNN
+- TDNN
+- Command Recognition
+license: "apache-2.0"
+datasets:
+- Urbansound8k
+metrics:
+- Accuracy
+
+---
+
+<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/>
+
+# Command Recognition with ECAPA embeddings on UrbanSoudnd8k
+
+This repository provides all the necessary tools to perform sound recognition with SpeechBrain using a model pretrained on UrbanSound8k.
+You can download the dataset [here](https://urbansounddataset.weebly.com/urbansound8k.html)
+The provided system can recognize the following 10 keywords:
+```
+dog_bark, children_playing, air_conditioner, street_music, gun_shot, siren, engine_idling, jackhammer, drilling, car_horn
+```
+
+For a better experience, we encourage you to learn more about
+[SpeechBrain](https://speechbrain.github.io). The given model performance on the test set is:
+
+| Release | Accuracy 1-fold (%)
+|:-------------:|:--------------:|
+| 04-06-21 | 75.5 | 
+
+
+## Pipeline description
+This system is composed of a ECAPA model coupled with statistical pooling. A classifier, trained with Categorical Cross-Entropy Loss, is applied on top of that.
+
+## 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).
+
+### Perform Sound Recognition
+
+```python
+import torchaudio
+from speechbrain.pretrained import EncoderClassifier
+classifier = EncoderClassifier.from_hparams(source="speechbrain/urbansound8k_ecapa", savedir="pretrained_models/gurbansound8k_ecapa")
+out_prob, score, index, text_lab = classifier.classify_file('speechbrain/urbansound8k_ecapa/dog_bark.wav')
+print(text_lab)
+```
+
+### 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 (8cab8b0c).
+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/UrbanSound8k/SoundClassification
+python train.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
+```
+
+You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1sItfg_WNuGX6h2dCs8JTGq2v2QoNTaUg?usp=sharing).
+
+### Limitations
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+
+#### Referencing ECAPA
+```@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},
+}
+```
+
+#### Referencing UrbanSound
+```@inproceedings{Salamon:UrbanSound:ACMMM:14,
+    Author = {Salamon, J. and Jacoby, C. and Bello, J. P.},
+    Booktitle = {22nd {ACM} International Conference on Multimedia (ACM-MM'14)},
+    Month = {Nov.},
+    Pages = {1041--1044},
+    Title = {A Dataset and Taxonomy for Urban Sound Research},
+    Year = {2014}}
+```
+
+
+
+# **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}
+}
+```

classifier.ckpt

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embedding_model.ckpt

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hyperparams.yaml

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+# ############################################################################
+# Model: ECAPA-TDNN for Language Identification
+# ############################################################################
+
+# Pretrain folder (HuggingFace)
+pretrained_path: speechbrain/lang-id-commonlanguage_ecapa
+
+# Feature parameters
+n_mels: 80
+
+# Output parameters
+out_n_neurons: 45 # Possible languages in the dataset
+
+
+# Model params
+compute_features: !new:speechbrain.lobes.features.Fbank
+    n_mels: !ref <n_mels>
+
+mean_var_norm: !new:speechbrain.processing.features.InputNormalization
+    norm_type: sentence
+    std_norm: False
+
+embedding_model: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
+    input_size: !ref <n_mels>
+    channels: [1024, 1024, 1024, 1024, 3072]
+    kernel_sizes: [5, 3, 3, 1, 1]
+    dilations: [1, 2, 3, 4, 1]
+    attention_channels: 128
+    lin_neurons: 192
+
+classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
+    input_size: 192
+    out_neurons: !ref <out_n_neurons>
+
+modules:
+    compute_features: !ref <compute_features>
+    mean_var_norm: !ref <mean_var_norm>
+    embedding_model: !ref <embedding_model>
+    classifier: !ref <classifier>
+
+label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder
+
+        
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        embedding_model: !ref <embedding_model>
+        classifier: !ref <classifier>
+        label_encoder: !ref <label_encoder>
+    paths:
+        embedding_model: !ref <pretrained_path>/embedding_model.ckpt
+        classifier: !ref <pretrained_path>/classifier.ckpt
+        label_encoder: !ref <pretrained_path>/label_encoder.txt

language_encoder.txt

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+'Arabic' => 0
+'Portuguese' => 1
+'Romansh_Sursilvan' => 2
+'Japanese' => 3
+'Ukranian' => 4
+'German' => 5
+'Chinese_China' => 6
+'Welsh' => 7
+'English' => 8
+'Kabyle' => 9
+'Kyrgyz' => 10
+'Georgian' => 11
+'Persian' => 12
+'French' => 13
+'Interlingua' => 14
+'Swedish' => 15
+'Spanish' => 16
+'Dhivehi' => 17
+'Kinyarwanda' => 18
+'Tatar' => 19
+'Hakha_Chin' => 20
+'Tamil' => 21
+'Greek' => 22
+'Latvian' => 23
+'Russian' => 24
+'Breton' => 25
+'Catalan' => 26
+'Maltese' => 27
+'Slovenian' => 28
+'Indonesian' => 29
+'Dutch' => 30
+'Chinese_Taiwan' => 31
+'Sakha' => 32
+'Polish' => 33
+'Czech' => 34
+'Romanian' => 35
+'Mangolian' => 36
+'Italian' => 37
+'Chinese_Hongkong' => 38
+'Estonian' => 39
+'Basque' => 40
+'Esperanto' => 41
+'Frisian' => 42
+'Turkish' => 43
+'Chuvash' => 44
+================
+'starting_index' => 0