Merge branch 'main' of https://huggingface.co/projecte-aina/stt-ca-citrinet-512 into main

README.md

@@ -9,24 +9,97 @@ tags:
 - automatic-speech-recognition
 - speech
 - audio
+- CTC
 - citrinet
 - pytorch
 - NeMo
-- hf-asr-leaderboard
+license: cc-by-4.0
 widget:
-- example_title: Common Voice sample 1
-  src: https://huggingface.co/projecte-aina/stt-ca-citrinet-512/samples/common_voice_ca_34667058.wav
+- example_title: CV sample 1
+  src: https://huggingface.co/projecte-aina/stt-ca-citrinet-512/tree/main/samples/common_voice_ca_34667058.wav
+model-index:
+- name: stt-ca-citrinet-512 
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Mozilla Common Voice 11.0
+      type: mozilla-foundation/common_voice_11_0
+      config: ca
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 6.684
 ---
 
-# Aina Project's Catalan multi-speaker text-to-speech model
+# Aina Project's Catalan text-to-speech model
 ## Model description
 
-This model was fine-tuned from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model using the [NeMo](https://github.com/NVIDIA/NeMo) toolkit
+This model transcribes audio samples in Catalan to lowercase text without punctuation. The model was fine-tuned from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model using the [NeMo](https://github.com/NVIDIA/NeMo) toolkit. It has around 36.5M parámeters and has been trained on [Common Voice 11.0](https://commonvoice.mozilla.org/en/datasets).
 
 ## Intended uses and limitations
 
-You can use this model for Automatic Speech Recognition (ASR) in catalan.
+You can use this model for Automatic Speech Recognition (ASR) in Catalan, to transcribe audio files in Catalan to plain text without punctuation.
 
+## How to use
+### Usage
+
+Requiered libraries:
+
+```bash
+pip install nemo_toolkit['all']
+```
+
+Clone the repository to download the model:
+
+```bash
+git clone https://huggingface.co/projecte-aina/stt-ca-citrinet-512
+```
+
+Given that `NEMO_PATH` is the path that points to the downloaded `stt-ca-citrinet-512.nemo` file, to do inference over a set of `.wav` files you should:
+
+```python
+# Load the model
+model = nemo_asr.models.EncDecCTCModel.restore_from(NEMO_PATH)
+
+# Create a list pointing to the audio files
+paths2audio_files = ["audio_1.wav", ..., "audio_n.wav"]
+
+# Fix the batch size to whatever number suits your purpose
+batch_size = 8
+
+# Transcribe the audio files
+transcriptions = model.transcribe(paths2audio_files=paths2audio_files,
+                                 batch_size=batch_size)
+# Visualize the transcriptions
+print(transcriptions)
+
+```
+
+## Training data
+
+This model has been trained on the training split of the Catalan version of [Common Voice 11.0](https://commonvoice.mozilla.org/en/datasets).
+
+## Training
+### Data preparation
+We have processed [Common Voice 11.0](https://commonvoice.mozilla.org/en/datasets) using the NeMo toolkit. We used [get_commonvoice_data.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/dataset_processing/get_commonvoice_data.py) to process the manifests and made a subsequent data cleaning step.
+
+After cleaning the dataset and normalizing the `ñ` character to `ny`, we have used the following charset to create the final NeMo manifests for training.
+```python
+['c', ' ', 'ó', 'g', 'a', 'o', 'ü', 'v', 'p', 't', "'", '—', 'f', 'k', 'à', 'ï', 'í', 'ú', 'd', 'l', 'z', 'é', 'w', 'm', 'r', 'n', 'y', '-', 'u', 'i', 'h', 'ç', 'e', '·', 'q', 'è', 'ò', 'j', 'x', 's', 'b']
+```
+
+### Training procedure
+This model was trained starting from a pre-trained Spanish [stt-es-citrinet-512](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_citrinet_512) model. The initial learning rate was set to 0.005 and the minimum lr for weight decay was set to 1e-7. 
+
+The model was trained for 90 steps and then continued training for another 90 steps starting from a learning rate of 1e-4.
+
+## Evaluation
+After evaluation on the test split of Common Voice 11.0 we have obtained a WER of 6.684.
 
 ## Additional information
 
@@ -39,12 +112,11 @@ For further information, send an email to [email protected]
 ### Copyright
 Copyright (c) 2022 Text Mining Unit at Barcelona Supercomputing Center 
 
-
 ### Licensing Information
-[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+[Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
 
 ### Funding
-This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
+This work was funded by the [Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
 
 
 ## Disclaimer