Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+ASR-Adapter-Colab_v175_wer_0.90.nemo filter=lfs diff=lfs merge=lfs -text

ASR-Adapter-Colab_v175_wer_0.90.nemo

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:130c28aea5fc40a12ad71bb28a055803a9df751463fc83bae8ff104767cbe99e
+size 505620480

README.md

@@ -0,0 +1,72 @@
+# NVIDIA Conformer-Transducer Large (ca-es)
+
+## Table of Contents
+<details>
+<summary>Click to expand</summary>
+
+- [Model Description](#model-description)
+- [Intended Uses and Limitations](#intended-uses-and-limitations)
+- [How to Get Started with the Model](#how-to-get-started-with-the-model)
+- [Training Details](#training-details)
+- [Citation](#citation)
+- [Additional Information](#additional-information)
+
+</details>
+
+## Summary
+
+The "stt_ca-es_conformer_transducer_large" is an acoustic model based on ["NVIDIA/stt_es_conformer_transducer_large"](https://huggingface.co/nvidia/stt_es_conformer_transducer_large/) suitable for Bilingual Catalan-Spanish Automatic Speech Recognition.
+
+## Model Description
+
+This model transcribes speech, and was fine-tuned on a Bilingual ca-es dataset comprising of 4000 hours. It is a "large" variant of Conformer-Transducer, with around 120 million parameters. We expaneded it is tokenizer vocab sise to be 5.5k t oinclude lowercase, uppercase, and punctuation
+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-transducer) for complete architecture details.
+
+## Intended Uses and Limitations
+
+This model can be used for Automatic Speech Recognition (ASR) in Catalan and Spanish. It is intended to transcribe audio files in Catalan and Spanish to plain text with punctuation.
+
+### Installation
+
+To use this model, install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest PyTorch version.
+```
+pip install nemo_toolkit['all']
+``` 
+
+### For Inference
+To transcribe audio in Catalan or in Spanish using this model, you can follow this example:
+
+
+```python
+import nemo.collections.asr as nemo_asr
+nemo_asr_model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(model)
+transcription = nemo_asr_model.transcribe([audio_path])[0].text
+print(transcription)
+```
+
+## Training Details
+
+### Training data
+
+The model was fine-tuned on bilingual datasets in Catalan and Spanish, for a total of 4k hours. Including:
+- [Parlament-Parla-v1](https://openslr.org/59/)
+- [multilingual_librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech)
+- [basque_parliament_1](https://huggingface.co/datasets/gttsehu/basque_parliament_1) 
+- [Voxpopuli](https://huggingface.co/datasets/facebook/voxpopuli) (The datasets will be made accessible shortly.)
+- [Coser](https://huggingface.co/datasets/johnatanebonilla/coser)
+- [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla)
+- [common_voice_16_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_0)
+
+### Training procedure
+
+This model is the result of finetuning the model ["projecte-aina/stt_ca-es_conformer_transducer_large"](https://huggingface.co/projecte-aina/stt_ca-es_conformer_transducer_large)
+
+
+
+### Results
+
+**Spanish WER:** 0.08
+**Catalan WER:** 0.10
+
+**Spanish CER:** 0.04
+**Catalan CER:** 0.05