Fix links in Segmentation tutorial

tutorials/tools/CTC_Segmentation_Tutorial.ipynb

@@ -280,7 +280,7 @@
         "* `max_length` argument - max number of words in a segment for alignment (used only if there are no punctuation marks present in the original text. Long non-speech segments are better for segments split and are more likely to co-occur with punctuation marks. Random text split could deteriorate the quality of the alignment.\n",
         "* out-of-vocabulary words will be removed based on pre-trained ASR model vocabulary, and the text will be changed to lowercase \n",
         "* sentences for alignment with the original punctuation and capitalization will be stored under  `$OUTPUT_DIR/processed/*_with_punct.txt`\n",
-        "* numbers will be converted from written to their spoken form with `num2words` package. For English, it's recommended to use NeMo normalization tool use `--use_nemo_normalization` argument (not supported if running this segmentation tutorial in Colab, see the text normalization tutorial: [`https://github.com/NVIDIA/NeMo-text-processing/blob/r1.19.0/tutorials/Text_(Inverse)_Normalization.ipynb`](https://colab.research.google.com/github/NVIDIA/NeMo-text-processing/blob/r1.19.0/tutorials/Text_(Inverse)_Normalization.ipynb) for more details). Even `num2words` normalization is usually enough for proper segmentation. However, it does not take audio into account. NeMo supports audio-based normalization for English, German and Russian languages that can be applied to the segmented data as a post-processing step. Audio-based normalization produces multiple normalization options. For example, `901` could be normalized as `nine zero one` or `nine hundred and one`. The audio-based normalization chooses the best match among the possible normalization options and the transcript based on the character error rate. See [https://github.com/NVIDIA/NeMo-text-processing/blob/main/nemo_text_processing/text_normalization/normalize_with_audio.py](https://github.com/NVIDIA/NeMo-text-processing/blob/r1.19.0/nemo_text_processing/text_normalization/normalize_with_audio.py) for more details.\n",
+        "* numbers will be converted from written to their spoken form with `num2words` package. For English, it's recommended to use NeMo normalization tool use `--use_nemo_normalization` argument (not supported if running this segmentation tutorial in Colab, see the text normalization tutorial: [`https://github.com/NVIDIA/NeMo-text-processing/blob/main/tutorials/Text_(Inverse)_Normalization.ipynb`](https://colab.research.google.com/github/NVIDIA/NeMo-text-processing/blob/main/tutorials/Text_(Inverse)_Normalization.ipynb) for more details). Even `num2words` normalization is usually enough for proper segmentation. However, it does not take audio into account. NeMo supports audio-based normalization for English, German and Russian languages that can be applied to the segmented data as a post-processing step. Audio-based normalization produces multiple normalization options. For example, `901` could be normalized as `nine zero one` or `nine hundred and one`. The audio-based normalization chooses the best match among the possible normalization options and the transcript based on the character error rate. See [https://github.com/NVIDIA/NeMo-text-processing/blob/main/nemo_text_processing/text_normalization/normalize_with_audio.py](https://github.com/NVIDIA/NeMo-text-processing/blob/main/nemo_text_processing/text_normalization/normalize_with_audio.py) for more details.\n",
         "\n",
         "### Audio preprocessing:\n",
         "* non '.wav' audio files will be converted to `.wav` format\n",