nvidia
/

stt_es_conformer_ctc_large

@@ -176,8 +176,10 @@ While deploying with [NVIDIA Riva](https://developer.nvidia.com/riva), you can c
 | Language Modeling | Training Dataset                                                             | MCV 7.0 Dev | MCV 7.0 Test | MLS Dev | MLS Test | Voxpopuli Dev | Voxpopuli Test | Fisher Dev | Fisher Test| Comment                                                |
 |-------------------|------------------------------------------------------------------------------|-------------|--------------|---------|----------|---------------|----------------|----------------|----------------|--------------------------------------------------------|
 | N-gram LM         | Spanish News Crawl corpus (50M sentences) + NeMo ASRSET training transcripts | 5.0         | 5.5          | 3.6     | 3.6      | 5.5           | 6.7 | 17.4 | 17.5            | N=4, beam_width=128, n_gram_alpha=0.8, n_gram_beta=1.5 |
 ## Limitations
 Since this model was trained on 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.
 ## Deployment with NVIDIA Riva
 For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded.
 Additionally, Riva provides:
@@ -185,6 +187,7 @@ Additionally, Riva provides:
 * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
 * Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support
 Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
 ## References
 - [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
 - [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)

 | Language Modeling | Training Dataset                                                             | MCV 7.0 Dev | MCV 7.0 Test | MLS Dev | MLS Test | Voxpopuli Dev | Voxpopuli Test | Fisher Dev | Fisher Test| Comment                                                |
 |-------------------|------------------------------------------------------------------------------|-------------|--------------|---------|----------|---------------|----------------|----------------|----------------|--------------------------------------------------------|
 | N-gram LM         | Spanish News Crawl corpus (50M sentences) + NeMo ASRSET training transcripts | 5.0         | 5.5          | 3.6     | 3.6      | 5.5           | 6.7 | 17.4 | 17.5            | N=4, beam_width=128, n_gram_alpha=0.8, n_gram_beta=1.5 |
 ## Limitations
 Since this model was trained on 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.
 ## Deployment with NVIDIA Riva
 For the best real-time accuracy, latency, and throughput, deploy the model with [NVIDIA Riva](https://developer.nvidia.com/riva), an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, at the edge, and embedded.
 Additionally, Riva provides:
 * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization
 * Streaming speech recognition, Kubernetes compatible scaling, and Enterprise-grade support
 Check out [Riva live demo](https://developer.nvidia.com/riva#demos).
 ## References
 - [1] [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
 - [2] [Google Sentencepiece Tokenizer](https://github.com/google/sentencepiece)