PraxySante
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stt_ca-es_conformer_transducer_large_fine_tuned

@@ -1,72 +1,115 @@
-# 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

+---
+datasets:
+- facebook/multilingual_librispeech
+- Parlament-Parla-v1
+- gttsehu/basque_parliament_1
+- facebook/voxpopuli
+- johnatanebonilla/coser_lv_full
+- collectivat/tv3_parla
+- mozilla-foundation/common_voice_16_0
+language:
+- es
+- ca
+metrics:
+- wer
+- cer
+tags:
+- automatic-speech-recognition
+- speech
+- multilingual
+- nemo
+model-index:
+  - name: Mohammed-Alzahrani-ai/stt_ca-es_conformer_transducer_large_fine_tuned
+    results:
+      - task:
+          type: automatic-speech-recognition
+          name: Automatic Speech Recognition
+        dataset:
+          type: automatic-speech-recognition
+          name: Combined (Parlament-Parla-v1, MLS, Voxpopuli, etc.)
+        metrics:
+          - name: WER (Spanish)
+            type: wer
+            value: 0.08
+          - name: CER (Spanish)
+            type: cer
+            value: 0.04
+          - name: WER (Catalan)
+            type: wer
+            value: 0.10
+          - name: CER (Catalan)
+            type: cer
+            value: 0.05
+---
+# 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