Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 language:
-- en
+- es
 thumbnail: null
 pipeline_tag: automatic-speech-recognition
 tags:
@@ -15,31 +15,31 @@ metrics:
 - wer
 - cer
 model-index:
-- name: asr-wav2vec2-commonvoice-14-en
+- name: asr-wav2vec2-commonvoice-14-es
   results:
   - task:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: CommonVoice Corpus 14.0 (English)
+      name: CommonVoice Corpus 14.0 (Spanish)
       type: mozilla-foundation/common_voice_14.0
-      config: en
+      config: es
       split: test
       args:
-        language: en
+        language: es
     metrics:
     - name: Test WER
       type: wer
-      value: '16.68'
+      value: '13.28'
 ---
 
 <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/>
 
-# wav2vec 2.0 with CTC trained on CommonVoice English (No LM)
+# wav2vec 2.0 with CTC trained on CommonVoice Spanish (No LM)
 
 This repository provides all the necessary tools to perform automatic speech
-recognition from an end-to-end system pretrained on CommonVoice (English Language) within
+recognition from an end-to-end system pretrained on CommonVoice (Spanish Language) within
 SpeechBrain. For a better experience, we encourage you to learn more about
 [SpeechBrain](https://speechbrain.github.io).
 
@@ -47,13 +47,13 @@ The performance of the model is the following:
 
 | Release | Test CER | Test WER | GPUs |
 |:-------------:|:--------------:|:--------------:| :--------:|
-| 15-08-23 | 7.92  | 16.86  | 1xV100 32GB |
+| 15-08-23 | 3.80  | 13.28  | 1xV100 32GB |
 
 ## Pipeline description
 
 This ASR system is composed of 2 different but linked blocks:
 - Tokenizer (unigram) that transforms words into unigrams and trained with
-the train transcriptions (train.tsv) of CommonVoice (en).  
+the train transcriptions (train.tsv) of CommonVoice (es).  
 - Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model (wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)) is combined with two DNN layers and finetuned on CommonVoice DE. 
 The obtained final acoustic representation is given to the CTC decoder.
 
@@ -71,13 +71,13 @@ pip install speechbrain transformers
 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 Spanish)
 
 ```python
 from speechbrain.pretrained import EncoderASR
 
-asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-14-en", savedir="pretrained_models/asr-wav2vec2-commonvoice-14-en")
-asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-14-en/example-en.wav")
+asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-14-es", savedir="pretrained_models/asr-wav2vec2-commonvoice-14-es")
+asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-14-es/example-es.wav")
 
 ```
 ### Inference on GPU
@@ -103,10 +103,10 @@ pip install -e .
 3. Run Training:
 ```bash
 cd recipes/CommonVoice/ASR/CTC/
-python train_with_wav2vec.py hparams/train_en_with_wav2vec.yaml --data_folder=your_data_folder
+python train_with_wav2vec.py hparams/train_es_with_wav2vec.yaml --data_folder=your_data_folder
 ```
 
-You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/ch10cnbhf1faz3w/AACdHFG65LC6582H0Tet_glTa?dl=0).
+You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/ejvzgl3d3g8g9su/AACYtbSWbDHvBr06lAb7A4mVa?dl=0).
 
 ### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.