Merge branch 'main' of https://huggingface.co/speechbrain/asr-crdnn-commonvoice-it into main

README.md

@@ -2,13 +2,14 @@
 language: "it"
 thumbnail:
 tags:
-- ASR
+- automatic-speech-recognition
 - CTC
 - Attention
 - pytorch
+- speechbrain
 license: "apache-2.0"
 datasets:
-- commonvoice
+- common_voice
 metrics:
 - wer
 - cer
@@ -18,7 +19,7 @@ metrics:
 
 This repository provides all the necessary tools to perform automatic speech
 recognition from an end-to-end system pretrained on CommonVoice (IT) within
-SpeechBrain. For a better experience we encourage you to learn more about
+SpeechBrain. For a better experience, we encourage you to learn more about
 [SpeechBrain](https://speechbrain.github.io). The given ASR model performance are:
 
 | Release | Test CER | Test WER | GPUs |
@@ -27,19 +28,19 @@ SpeechBrain. For a better experience we encourage you to learn more about
 
 ## Pipeline description
 
-This ASR system is composed with 2 different but linked blocks:
+This ASR system is composed of 2 different but linked blocks:
 1. Tokenizer (unigram) that transforms words into subword units and trained with
 the train transcriptions (train.tsv) of CommonVoice (IT).
-3. Acoustic model (CRDNN + CTC/Attention). The CRDNN architecture is made of
-N blocks of convolutional neural networks with normalisation and pooling on the
+2. 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
 the final acoustic representation that is given to the CTC and attention decoders.
 
 ## Intended uses & limitations
 
-This model has been primilarly developed to be run within SpeechBrain as a pretrained ASR model
+This model has been primarily developed to be run within SpeechBrain as a pretrained ASR model
 for the Italian language. Thanks to the flexibility of SpeechBrain, any of the 2 blocks
-detailed above can be extracted and connected to you custom pipeline as long as SpeechBrain is
+detailed above can be extracted and connected to your custom pipeline as long as SpeechBrain is
 installed.
 
 ## Install SpeechBrain
@@ -47,19 +48,19 @@ installed.
 First of all, please install SpeechBrain with the following command:
 
 ```
-pip install \\we hide ! SpeechBrain is still private :p
+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
+### Transcribing your own audio files (in Italian)
 
 ```python
 from speechbrain.pretrained import EncoderDecoderASR
 
-asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-it")
-asr_model.transcribe_file("path_to_your_file.wav")
+asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-commonvoice-it", savedir="pretrained_models/asr-crdnn-commonvoice-it")
+asr_model.transcribe_file("speechbrain/asr-crdnn-commonvoice-it/example-it.wav")
 
 ```
 
@@ -72,6 +73,6 @@ asr_model.transcribe_file("path_to_your_file.wav")
     year = {2021},
     publisher = {GitHub},
     journal = {GitHub repository},
-    howpublished = {\url{https://github.com/speechbrain/speechbrain}},
+    howpublished = {\\url{https://github.com/speechbrain/speechbrain}},
   }
 ```