Update README.md

README.md

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 ---
 license: cc-by-4.0
+language:
+- eu
+library_name: nemo
+datasets:
+- mozilla-foundation/common_voice_16_1
+- gttsehu/basque_parliament_1
+- openslr
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- CTC
+- Conformer
+- NeMo
+- pytorch
+- Transformer
+model-index:
+- name: stt_eu_conformer_ctc_large
+  results:
+  - task:
+      type: Automatic Speech Recognition
+      name: speech-recognition
+    dataset:
+      name: Mozilla Common Voice 16.1
+      type: mozilla-foundation/common_voice_16_1
+      config: eu
+      split: test
+      args:
+        language: eu
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 2.42
+  - task:
+      type: Automatic Speech Recognition
+      name: speech-recognition
+    dataset:
+      name: Basque Parliament
+      type: gttsehu/basque_parliament_1
+      config: eu
+      split: test
+      args:
+        language: eu
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 4.21
+  - task:
+      type: Automatic Speech Recognition
+      name: speech-recognition
+    dataset:
+      name: Basque Parliament
+      type: gttsehu/basque_parliament_1
+      config: eu
+      split: validation
+      args:
+        language: eu
+    metrics:
+    - name: Dev WER
+      type: wer
+      value: 4.3
 ---
+
+# HiTZ/Aholab's Basque Speech-to-Text model
+## Model Description
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+| [![Model architecture](https://img.shields.io/badge/Model_Arch-Conformer--CTC-lightgrey#model-badge)](#model-architecture)
+| [![Model size](https://img.shields.io/badge/Params-121M-lightgrey#model-badge)](#model-architecture)
+| [![Language](https://img.shields.io/badge/Language-eu-lightgrey#model-badge)](#datasets)
+
+This model transcribes speech in lowercase Basque alphabet including spaces, and was trained on a composite dataset comprising of 548 hours of Basque speech. The model was fine-tuned from a pre-trained Spanish [stt_es_conformer_ctc_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_conformer_ctc_large) model using the [Nvidia NeMo](https://github.com/NVIDIA/NeMo) toolkit.  It is a non-autoregressive "large" variant of Conformer, with around 121 million parameters.
+See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc) for complete architecture details.
+
+## Usage
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest PyTorch version.
+
+```bash
+pip install nemo_toolkit['all']
+```
+
+### Transcribing using Python
+Clone repository to download the model:
+
+```bash
+git clone https://huggingface.co/asierhv/stt_eu_conformer_ctc_large
+```
+
+Given `NEMO_MODEL_FILEPATH` is the path that points to the downloaded `stt_eu_conformer_ctc_large.nemo` file.
+
+```python
+import nemo.collections.asr as nemo_asr
+
+# Load the model
+asr_model = nemo_asr.models.EncDecCTCModelBPE.restore_from(NEMO_MODEL_FILEPATH)
+
+# Create a list pointing to the audio files
+audio = ["audio_1.wav","audio_2.wav", ..., "audio_n.wav"]
+
+# Fix the batch_size to whatever number suits your purpouse
+batch_size = 8
+
+# Transcribe the audio files
+transcriptions = asr_model.transcribe(audio=audio, batch_size=batch_size)
+
+# Visualize the transcriptions
+print(transcriptions)
+```
+#### Change decoding strategy
+Optionally you can add some lines before transcribing the audio to change the decoding strategy and use Beam Search with N-gram Language Model. The previous installation of the beam search decoders has been made using the [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh) provided by the NeMo Toolkit [3]. Given `KENLM_MODEL_FILEPATH` is the path that points to the downloaded `kenlm_unigram_v256_model.bin` file.
+
+```python
+from omegaconf import OmegaConf, open_dict
+
+with open_dict(asr_model.cfg):
+  asr_model.cfg.decoding.strategy = "beam"
+  asr_model.cfg.decoding.beam.beam_size = 32 # Desired Beam Size
+  asr_model.cfg.decoding.beam.beam_alpha = 1 # Desired Beam Alpha
+  asr_model.cfg.decoding.beam.beam_beta = 1 # Desired Beam Beta
+  asr_model.cfg.decoding.beam.kenlm_path = KENLM_MODEL_FILEPATH
+asr_model.change_decoding_strategy(asr_model.cfg.decoding)
+```
+
+## Input
+This model accepts 16000 kHz Mono-channel Audio (wav files) as input.
+
+## Output
+This model provides transcribed speech as a string for a given audio sample.
+
+## Model Architecture
+Conformer-CTC model is a non-autoregressive variant of Conformer model [1] for Automatic Speech Recognition which uses CTC loss/decoding instead of Transducer. You may find more info on the detail of this model here: [Conformer-CTC Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/models.html#conformer-ctc). 
+
+## Training
+### Data preparation
+This model has been trained on a composite dataset comprising 548 hours of Basque speech that contains:
+- A processed subset of the `validated` split of the basque version of the public dataset [Mozilla Common Voice 16.1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1): We have processed the `validated` split, which originally contains the `train`, `dev` and `test` splits, to create a subset free of sentences equal to the ones that are in the `test` split, to avoid leakage.
+- The `train_clean` split of the basque version of the public dataset [Basque Parliament](https://huggingface.co/datasets/gttsehu/basque_parliament_1)
+- A processed subset of the basque version of the public dataset [OpenSLR](https://huggingface.co/datasets/openslr#slr76-crowdsourced-high-quality-basque-speech-data-set): This subset has been cleaned from numerical characters and acronyms.
+
+The composite dataset for training has been precisely cleaned from any sentence that equals the ones in the `test` datasets where the WER metrics will be computed.
+
+### Training procedure
+This model was trained starting from the pre-trained Spanish model [stt_es_conformer_ctc_large](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_es_conformer_ctc_large) over several hundred of epochs in a GPU device, using the NeMo toolkit [3]
+The tokenizer for these model was built using the text transcripts of the composite train dataset with this [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py), with a total of 256 basque language tokens.
+
+## Performance
+Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding in the following table.
+| Tokenizer             | Vocabulary Size | MCV 16.1 Test | Basque Parliament Test | Basque Parliament Dev | Train Dataset                |
+|-----------------------|-----------------|---------------|------------------------|-----------------------|------------------------------|
+| SentencePiece Unigram | 256             | 4.72          | 4.51                   | 4.85                  | Composite Dataset (548 h)    |
+
+A N-gram Language model has been trained using the [script](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/train_kenlm.py) provided in the NeMo Toolkit [3] with a corpus comprissed of 27 million basque language sentences from accesible open sources like:
+- Tatoeba, OpenSubtitles, TED, GlobalVoices, and other corpora from [OPUS](https://opus.nlpl.eu/)
+- [Wikipedia dump (2023-09-20)](https://dumps.wikimedia.org/euwiki/)
+- [EusCrawl 1.0](https://ixa.ehu.eus/euscrawl/)
+
+Performances of the ASR models are reported in terms of Word Error Rate (WER%) with beam-search decoding with N-gram LM in the following table.
+
+| N | Beam Size | Beam Alpha | Beam Beta | MCV 16.1 Test | Basque Parliament Test | Basque Parliament Dev |
+|---|-----------|------------|-----------|---------------|------------------------|-----------------------|
+| 6 | 32        | 1          | 1         | 2.42          | 4.21                   | 4.3                   |
+
+## Limitations
+Since this model was trained on almost publicly available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
+
+# Aditional Information
+## Author
+HiTZ Basque Center for Language Technology - Aholab Signal Processing Laboratory, University of the Basque Country UPV/EHU.
+
+## Copyright
+Copyright (c) 2024 HiTZ Basque Center for Language Technology - Aholab Signal Processing Laboratory, University of the Basque Country UPV/EHU.
+
+## Licensing Information
+[Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
+
+## Funding
+This project with reference 2022/TL22/00215335 has been parcially funded by the Ministerio de Transformación Digital and by the Plan de Recuperación, Transformación y Resiliencia – Funded by the European Union – NextGenerationEU [ILENIA](https://proyectoilenia.es/) and by the project [IkerGaitu](https://www.hitz.eus/iker-gaitu/) funded by the Basque Government.
+
+## References
+- [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
+- [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)
+- [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
+
+## Disclaimer
+<details>
+<summary>Click to expand</summary>
+The models published in this repository are intended for a generalist purpose and are available to third parties. These models may have bias and/or any other undesirable distortions.
+
+When third parties, deploy or provide systems and/or services to other parties using any of these models (or using systems based on these models) or become users of the models, they should note that it is their responsibility to mitigate the risks arising from their use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
+
+In no event shall the owner and creator of the models (Aholab Signal Processing Laboratory from HiTZ: Basque Center for Language Technology at the UPV/EHU) be liable for any results arising from the use made by third parties of these models.