Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 language:
-- en
+- rw
 thumbnail: null
 tags:
 - automatic-speech-recognition
@@ -11,17 +11,16 @@ tags:
 license: apache-2.0
 datasets:
 - common_voice
-
 metrics:
-  - name: Test WER
-    type: wer
-    value: ' 23.88'
+- name: Test WER
+  type: wer
+  value: ' 29.22'
 ---
 
 <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/>
 
-# CRDNN with CTC/Attention trained on CommonVoice 14.0 English (No LM)
+# CRDNN with CTC/Attention trained on CommonVoice 14.0 Kinyarwanda (No LM)
 This repository provides all the necessary tools to perform automatic speech
 recognition from an end-to-end system pretrained on CommonVoice (German Language) within
 SpeechBrain. For a better experience, we encourage you to learn more about
@@ -30,16 +29,14 @@ The performance of the model is the following:
 
 | Release | Test CER | Test WER | GPUs |
 |:-------------:|:--------------:|:--------------:| :--------:|
-| 15.08.23 | 12.76 | 23.88 | 1xV100 32GB |
+| 15.08.23 | 10.80 | 29.22 | 1xV100 32GB |
 
-## Credits
-The model is provided by [vitas.ai](https://www.vitas.ai/).
 
 ## Pipeline description
 This ASR system is composed of 2 different but linked blocks:
 
 - Tokenizer (unigram) that transforms words into subword units and trained with
-the train transcriptions (train.tsv) of CommonVoice (en).
+the train transcriptions (train.tsv) of CommonVoice (rw).
 - Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
 N blocks of convolutional neural networks with normalization and pooling on the
 frequency domain. Then, a bidirectional LSTM is connected to a final DNN to obtain
@@ -58,12 +55,12 @@ pip install speechbrain
 Please notice that we encourage you to read our tutorials and learn more about
 [SpeechBrain](https://speechbrain.github.io).
 
-### Transcribing your own audio files (in English)
+### Transcribing your own audio files (in Kinyarwanda)
 
 ```python
 from speechbrain.pretrained import EncoderDecoderASR
-asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/speechbrain/asr-crdnn-commonvoice-14-en", savedir="pretrained_models/speechbrain/asr-crdnn-commonvoice-14-en")
-asr_model.transcribe_file("speechbrain/speechbrain/asr-crdnn-commonvoice-14-en/example-en.wav")
+asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/speechbrain/asr-crdnn-commonvoice-14-rw", savedir="pretrained_models/speechbrain/asr-crdnn-commonvoice-14-rw")
+asr_model.transcribe_file("speechbrain/speechbrain/asr-crdnn-commonvoice-14-rw/example-rw.wav")
 ```
 
 ### Inference on GPU
@@ -97,10 +94,10 @@ pip install -e .
 
 ```
 cd recipes/CommonVoice/ASR/seq2seq
-python train.py hparams/train_en.yaml --data_folder=your_data_folder
+python train.py hparams/train_rw.yaml --data_folder=your_data_folder
 ```
 
-You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/zgatirb118f79ef/AACmjh-D94nNDWcnVI4Ef5K7a?dl=0)
+You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/i1fv4f8miilqgii/AAB3gE97kmFDA0ISkIDSUW_La?dl=0)
 
 ### Limitations