This collection contains ContextNet-1024 (around 140M parameters) trained on French NeMo ASRSet with over 1500 hours of French speech.
It utilizes a Google SentencePiece  tokenizer with vocabulary size 1024, and transcribes text in lower case French alphabet along with spaces, apostrophes, and hyphenation.
ContextNet  model is an autoregressive, transducer based Automatic Speech Recognition model. You may find more info on the detail of this model here: ContextNet Model.
The tokenizers for these models were built using the text transcripts of the train set with this script.
All the models in this collection are trained on a composite dataset (NeMo ASRSET) comprising of over fifteen hundred hours of cleaned French speech:
The tokenizer for this model was built using text corpus provided with the train dataset.
We build a Google Sentencepiece Tokenizer  with the following script :
python [NEMO_GIT_FOLDER]/scripts/tokenizers/process_asr_text_tokenizer.py \ --manifest="train_manifest.json" \ --data_root="" \ --vocab_size=1024 \ --tokenizer="spe" \ --spe_type="unigram" \ --spe_character_coverage=1.0 \ --no_lower_case \ --log
The performance of Automatic Speech Recognition models is measuring 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 -
Note that these evaluation datasets have been filtered and preprocessed to only contain French alphabet characters and are removed of punctuation outside of hyphenation and apostrophe.
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_contextnet_1024")
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py \ pretrained_name="stt_fr_contextnet_1024" \ audio_dir=""
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.