Issues with Tokenizer in TTS Es Multispeaker FastPitch HiFiGAN Model - Problems with Date Tokenization
A few days ago, a new Spanish tokenizer was uploaded to NGC that works properly, so I would consider this issue closed.

https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/models/normalization_es_es/files
Hello,

I am currently working on integrating a Latin American Spanish text-to-speech (TTS) model into NVIDIA Riva, specifically using the [tts_es_multispeaker_fastpitchhifigan](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/tts_es_multispeaker_fastpitchhifigan) model from NVIDIA NGC. After converting the model to a .riva extension using nemo2riva, I downloaded the tokenizer and verbalizer from Inverse [Normalization ES-US](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/inverse_normalization_es_us).

I have deployed it in riva with
```bash
docker run --init -it --rm --gpus '"'"device=0"'"'   -v $(pwd):/data  -v riva-model-repo:/data-volumen  -e "MODEL_DEPLOY_KEY=tlt_encode"   --name riva-service-maker   nvcr.io/nvidia/riva/riva-speech:2.13.0-servicemaker

riva-build speech_synthesis  tts_es_hifigan_ft_fastpitch_multispeaker.rmir:tlt_encode tts_es_fastpitch_multispeaker.riva:tlt_encode  tts_es_hifigan_ft_fastpitch_multispeaker.riva:tlt_encode  --voice_name Latin-American-Spanish --wfst_tokenizer_model=tokenize_and_classify.far --wfst_verbalizer_model=verbalize.far --sample_rate 44100 --language_code es-US --num_speakers=174 --phone_set=ipa   --subvoices 0:0,1:1,2:2,3:3,4:4,5:5,6:6,7:7,8:8,9:9,10:10,11:11,12:12,13:13,14:14,15:15,16:16,17:17,18:18,19:19,20:20,21:21,22:22,23:23,24:24,25:25,26:26,27:27,28:28,29:29,30:30,31:31,32:32,33:33,34:34,35:35,36:36,37:37,38:38,39:39,40:40,41:41,42:42,43:43,44:44,45:45,46:46,47:47,48:48,49:49,50:50,51:51,52:52,53:53,54:54,55:55,56:56,57:57,58:58,59:59,60:60,61:61,62:62,63:63,64:64,65:65,66:66,67:67,68:68,69:69,70:70,71:71,72:72,73:73,74:74,75:75,76:76,77:77,78:78,79:79,80:80,81:81,82:82,83:83,84:84,85:85,86:86,87:87,88:88,89:89,90:90,91:91,92:92,93:93,94:94,95:95,96:96,97:97,98:98,99:99,100:100,101:101,102:102,103:103,104:104,105:105,106:106,107:107,108:108,109:109,110:110,111:111,112:112,113:113,114:114,115:115,116:116,117:117,118:118,119:119,120:120,121:121,122:122,123:123,124:124,125:125,126:126,127:127,128:128,129:129,130:130,131:131,132:132,133:133,134:134,135:135,136:136,137:137,138:138,139:139,140:140,141:141,142:142,143:143,144:144,145:145,146:146,147:147,148:148,149:149,150:150,151:151,152:152,153:153,154:154,155:155,156:156,157:157,158:158,159:159,160:160,161:161,162:162,163:163,164:164,165:165,166:166,167:167,168:168,169:169,170:170,171:171,172:172,173:173

riva-deploy -f tts_es_hifigan_ft_fastpitch_multispeaker.rmir:tlt_encode /data/models

```

However, I've encountered a significant issue: the tokenizer struggles with words that require tokenization, especially dates. Instead of processing and pronouncing these elements correctly, it either skips them or handles them incorrectly. This is in stark contrast to the performance of the default models in riva_quickstart_v2.13.0, where the es-ES model handles such tokenization challenges efficiently.

According to the  [NVIDIA NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/inverse_normalization_es_us), the text normalizers were built using NeMo, but they do not seem to function correctly for dates, unlike the default models for es-ES.

I would like to inquire:

- Are there any pre-trained text normalizers for Spanish that work well with dates?
- Could the issue with date tokenization be arising from some other aspect of the configuration or implementation?
- Any guidance or suggestions to resolve this issue with date tokenization would be greatly appreciated.

Best regards.