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• Samrómur-Adolescents Kaldi Recipe 22.06

  2 resources

  The "Samrómur-Adolescents Kaldi Recipe 22.06" is a code recipe intended to show how to integrate the adolescent portion of the corpus "Samrómur Children's Icelandic Speech Data 21.09" [1] and the "Icelandic Language Models with Pronunciations 22.01" [2] to create automatic speech recognition systems using the Kaldi toolkit [3].

  Use "Samrómur-Adolescents Kaldi Recipe 22.06"

• Slovene Conformer CTC BPE E2E Automated Speech Recognition model PROTOVERB-ASR-E2E 1.0

  2 resources

  This Conformer CTC BPE E2E Automated Speech Recognition model was trained following the NVIDIA NeMo Conformer-CTC fine-tuning recipe (for details see the official NVIDIA NeMo NMT documentation, https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/intro.html, and NVIDIA NeMo GitHub repository https://github.com/NVIDIA/NeMo). It provides functionality for transcribing Slovene speech to text. The starting point was the Conformer CTC BPE E2E Automated Speech Recognition model RSDO-DS2-ASR-E2E 2.0, which was fine-tuned on the Protoverb closed dataset. The model was fine-tuned for 20 epochs, which improved the performance on the Protoverb test dataset for 9.8% relative WER, and for 3.3% relative WER on the Slobench dataset.

  Use "Slovene Conformer CTC BPE E2E Automated Speech Recognition model PROTOVERB-ASR-E2E 1.0"

• Slovene Conformer CTC BPE E2E Automated Speech Recognition model RSDO-DS2-ASR-E2E 2.0

  2 resources

  This Conformer CTC BPE E2E Automated Speech Recognition model was trained following the NVIDIA NeMo Conformer-CTC recipe (for details see the official NVIDIA NeMo NMT documentation, https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/intro.html, and NVIDIA NeMo GitHub repository https://github.com/NVIDIA/NeMo). It provides functionality for transcribing Slovene speech to text. The training, development and test datasets were based on the Artur dataset and consisted of 630.38, 16.48 and 15.12 hours of transcribed speech in standardised form, respectively. The model was trained for 200 epochs and reached WER 0.0429 on the development and WER 0.0558 on the test dataset.

  Use "Slovene Conformer CTC BPE E2E Automated Speech Recognition model RSDO-DS2-ASR-E2E 2.0"

• MAFIA (Match-Finder Aligner): A speech/text aligning tool (22.06)

  2 resources

  MAFIA is the acronym for Match-Finder Aligner. The MAFIA aligner is a software tool destined to automatically create ASR corpora out of speech files along with scripts reflecting what is spoken in such speech files. If the text of the scripts is not completely accurate, MAFIA will infer a transcription using automatic speech recognition.

  Use "MAFIA (Match-Finder Aligner): A speech/text aligning tool (22.06)"

• NeMo Conformer CTC BPE E2E Automated Speech Recognition service RSDO-DS2-ASR-E2E-API 1.1

  2 resources

  Automated Speech Recognition service for NeMo Conformer CTC BPE E2E models. For more details about building such models, see the official NVIDIA NeMo documentation (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/intro.html) and NVIDIA NeMo GitHub (https://github.com/NVIDIA/NeMo). A model for automated speech recognition of Slovene speech can be downloaded from http://hdl.handle.net/11356/1740. The service accepts as input audio files in WAV 16kHz, 16bit PCM, mono format. The maximal accepted audio duration is 300s. Note that transcription of one 300s audio file on cpu will take advantage of all available cores, consume up to 16GB RAM and may take ~180s (on a system with 24 vCPU). See the service README.md for further details.

  Use "NeMo Conformer CTC BPE E2E Automated Speech Recognition service RSDO-DS2-ASR-E2E-API 1.1"