This collection contains large size versions of Conformer-Transducer (around 120M parameters) trained on NeMo ASRSet with over 1500 hours of French speech. The models was initialized with parameters from an English model and finetuned for French. Parameters from the three checkpoints with lowest Word Error Rate were then averaged to produce the final model.
Utilizes a Google SentencePiece  tokenizer with vocabulary size 128, and transcribes text in lower case French alphabet along with spaces, apostrophes, and hyphens.
Conformer-Transducer model is an autoregressive variant of Conformer model  for Automatic Speech Recognition which uses Transducer loss/decoding. You may find more info on the detail of this model here: Conformer-Transducer Model.
The tokenizers for these models were built using the text transcripts of the train set with this script.
The three highest performing checkpoint of the model were averaged with this script.
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprised of over a thousand hours of French speech:
(Note: The MultiliingualLibreSpeech set includes orthography of both pre- and post- 1990 reform, which may affect predictability of hyphenation.)
The tokenizer for this model was built using text corpus provided with the train dataset.
We build a token set with the following script:
python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \ --manifest="train_manifest.json" \ --data_root="" \ --vocab_size=128 \ --tokenizer="spe" \ --spe_type="unigram" \ --spe_character_coverage=1.0 \ --no_lower_case \ --log
The performance of Automatic Speech Recognition models is measured using Word Error Rate. Since this dataset is trained on multiple domains and a much larger corpus, it will generally perform better at transcribing audio in general.
The latest model obtains the following greedy scores on the following evaluation datasets
6.85 % on MCV7.0 dev 7.95 % on MCV7.0 test 5.05 % on MLS dev 4.10 % on MLS test
The model is available for use in the NeMo toolkit , and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained(model_name="stt_fr_conformer_transducer_large")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_de_conformer_transducer_large" \ audio_dir="PATH_TO_AUDIO_FILES"
This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
This model provides transcribed speech as a string for a given audio sample.
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.
Further, since portions of the training set contain text from both pre- and post- 1990 orthographic reform, regularity of punctuation may vary between the two styles. For downstream tasks requiring more consistency, finetuning or downstream processing may be required.
References  Conformer: Convolution-augmented Transformer for Speech Recognition
 Google Sentencepiece Tokenizer
 NVIDIA NeMo Toolkit