Update README.md

README.md

@@ -42,7 +42,7 @@ model-index:
 # 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 (German 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).
 
@@ -56,8 +56,8 @@ The performance of the model is the following:
 
 This ASR system is composed of 2 different but linked blocks:
 - Tokenizer (char) that transforms words into chars and trained with
-the train transcriptions (train.tsv) of CommonVoice (DE).  
-- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-large-xlsr-53-german](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-german)) is combined with two DNN layers and finetuned on CommonVoice DE. 
+the train transcriptions (train.tsv) of CommonVoice (ES).  
+- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-large-xlsr-53-spanish](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-spanish)) is combined with two DNN layers and finetuned on CommonVoice DE. 
 The obtained final acoustic representation is given to the CTC decoder.
 
 The system is trained with recordings sampled at 16kHz (single channel).
@@ -74,20 +74,18 @@ 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 German)
+### Transcribing your own audio files (in Spanish)
 
 ```python
 from speechbrain.pretrained import EncoderASR
 
-asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-de", savedir="pretrained_models/asr-wav2vec2-commonvoice-de")
-asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-de/example-de.wav")
+asr_model = EncoderASR.from_hparams(source="Voyager1/asr-wav2vec2-commonvoice-es", savedir="pretrained_models/asr-wav2vec2-commonvoice-es")
+asr_model.transcribe_file("Voyager1/asr-wav2vec2-commonvoice-es/example-es.wav")
 
 ```
 ### Inference on GPU
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
-## Parallel Inference on a Batch
-Please, [see this Colab notebook](https://colab.research.google.com/drive/1hX5ZI9S4jHIjahFCZnhwwQmFoGAi3tmu?usp=sharing) to figure out how to transcribe in parallel a batch of input sentences using a pre-trained model.
 
 ### Training
 The model was trained with SpeechBrain.
@@ -106,11 +104,9 @@ pip install -e .
 3. Run Training:
 ```bash
 cd recipes/CommonVoice/ASR/seq2seq
-python train.py hparams/train_de_with_wav2vec.yaml --data_folder=your_data_folder
+python train.py hparams/train_es_with_wav2vec.yaml --data_folder=your_data_folder
 ```
 
-You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/19G2Zm8896QSVDqVfs7PS_W86-K0-5xeC?usp=sharing).
-
 ### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.