STT It FastConformer Hybrid Transducer-CTC Large P&C

Model Overview

This collection contains large size versions of Hybrid FastConformer Transducer-CTC (around 120M parameters) trained on NeMo ASRSet with around 500 hours of Italian speech.

It utilizes a Google SentencePiece [1] tokenizer with vocabulary size 512, and transcribes speech in upper and lower case Italian alphabet along with spaces, periods, commas, question marks and apostrophes.

Model Architecture

FastConformer is an optimized version of the Conformer model [2] with 8x depthwise-separable convolutional downsampling. The model is trained in a multitask setup with joint Transducer and CTC decoder loss. You may find more information on the details of FastConformer here: Fast-Conformer Model and about Hybrid Transducer-CTC training here: Hybrid Transducer-CTC

Training

The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this example script and this base config.

The tokenizers for these models were built using the text transcripts of the train set with this script.

Datasets

All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of 487 hours of Italian speech:

• Mozilla Common Voice 12.0 (Italian) - 220 hours after data cleaning. Speech Data Processor config used to prepare this data is here.
• Multilingual LibriSpeech (Italian) - 214 hours after data cleaning. Speech Data Processor config used to prepare this data is here.
• VoxPopuli transcribed subset (Italian) - 53 hours after data cleaning. Speech Data Processor config used to prepare this data is here.

Tokenizer Construction

The tokenizer for this model was built using text corpus provided with the train dataset.

We build a Google Sentencepiece Tokenizer [1] with the following script :

python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \
  --manifest="train_manifest.json" \
  --data_root="<OUTPUT DIRECTORY FOR TOKENIZER>" \
  --vocab_size=512 \
  --tokenizer="spe" \
  --spe_type="bpe" \
  --spe_character_coverage=1.0 \
  --no_lower_case \
  --log

Performance

The list of the available models in this collection is shown in the following table. Performances of the ASR models are reported in terms of Word Error Rate (WER%) with greedy decoding. We report WER with punctuation and capitalization (with P&C) as well as when punctuation and capitalization is removed from both predicted and target text (no P&C). The tables below contain results using both Transducer and CTC decoders.

No P&C

With P&C

* We use only a subset of dev/test sets with P&C restored from the original books

How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.from_pretrained("stt_it_fastconformer_hybrid_large_pc")

Transcribing text with this model

With Transducer decoder:

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \
  pretrained_name="stt_it_fastconformer_hybrid_large_pc" \
  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"

With CTC decoder:

python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \
  pretrained_name="stt_it_fastconformer_hybrid_large_pc" \
  decoder_type=ctc \
  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>" \

Input

This model accepts 16 KHz Mono-channel Audio (wav files) as input.

Output

This model provides transcribed speech as a string for a given audio sample.

Limitations

Since this model was trained on publically 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. The model only outputs the following punctuation marks: '.', ',', '?' and hence might not do well in scenarios where other punctuation marks are expected.

References

[1] Google Sentencepiece Tokenizer

[2] Conformer: Convolution-augmented Transformer for Speech Recognition

[3] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the CC-BY-4 License unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the CC-BY-4 License.