Cnam-LMSSC

27 models • 1 total models in database
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wav2vec2-french-phonemizer

license:mit
38,422
8

wav2vec2-french-phonemizer-v2

license:mit
2,146
1

wav2vec2-italian-phonemizer

license:mit
65
1

phonemizer_headset_microphone

license:mit
12
4

phonemizer_temple_vibration_pickup

license:mit
7
2

EBEN_soft_in_ear_microphone

license:mit
7
2

phonemizer_throat_microphone

license:mit
6
2

phonemizer_forehead_accelerometer

license:mit
4
2

phonemizer_rigid_in_ear_microphone

license:mit
3
2

EBEN_temple_vibration_pickup

license:mit
3
2

EBEN_reverse_temple_vibration_pickup

license:mit
3
0

EBEN_reverse_forehead_accelerometer

license:mit
3
0

EBEN_noisy_forehead_accelerometer

license:mit
3
0

phonemizer_soft_in_ear_microphone

license:mit
2
2

EBEN_reverse_rigid_in_ear_microphone

license:mit
2
0

EBEN_noisy_rigid_in_ear_microphone

license:mit
2
0

EBEN_noisy_temple_vibration_pickup

license:mit
2
0

EBEN_forehead_accelerometer

license:mit
1
2

EBEN_rigid_in_ear_microphone

license:mit
1
2

EBEN_throat_microphone

license:mit
1
2

EBEN_reverse_soft_in_ear_microphone

license:mit
1
0

EBEN_noisy_throat_microphone

license:mit
1
0

Vibravox EBEN Models

Master Model Card: Vibravox Audio Bandwidth extension Models This master model card serves as an entry point for exploring multiple audio bandwidth extension (BWE) models trained on different sensor data from the Vibravox dataset. These models are designed to to enhance the audio quality of body-conducted captured speech, by denoising and regenerating mid and high frequencies from low frequency content only. The models are trained on specific sensors to address various audio capture scenarios using body conducted sound and vibration sensors. Disclaimer Each of these models has been trained for specific non-conventional speech sensors and is intended to be used with in-domain data. Please be advised that using these models outside their intended sensor data may result in suboptimal performance. Usage All models are trained using Configurable EBEN (see publication in IEEE TASLP - arXiv link) and adapted to different sensor inputs. They are intended to be used at a sample rate of 16kHz. Training Procedure Detailed instructions for reproducing the experiments are available on the jhauret/vibravox Github repository and in the VibraVox paper on arXiV. The following models are available, each trained on a different sensor on the `speechclean` or synthetically mixed `speechclean` and `speechless-noisy` subsets of (https://huggingface.co/datasets/Cnam-LMSSC/vibravox): | Transducer | EBEN configuration | Huggingface model trained on speech-clean link | Huggingface model trained on synthetically mixed speech-clean and speechless-noisy link | |:---------------------------|:---------------------|:---------------------|:---------------------| | In-ear comply foam-embedded microphone | M=4,P=2,Q=4 |EBENsoftinearmicrophone |EBENnoisysoftinearmicrophone| | In-ear rigid earpiece-embedded microphone | M=4,P=2,Q=4 |EBENrigidinearmicrophone | EBENnoisyrigidinearmicrophone| | Forehead miniature vibration sensor | M=4,P=4,Q=4 |EBENforeheadaccelerometer | EBENnoisyforeheadaccelerometer| | Temple vibration pickup | M=4,P=1,Q=4 |EBENtemplevibrationpickup | EBENnoisytemplevibrationpickup| | Laryngophone | M=4,P=2,Q=4 |EBENthroatmicrophone | EBENnoisythroatmicrophone|

license:mit
0
5

vibravox_phonemizers

license:mit
0
4

wav2vec2-spanish-phonemizer

license:mit
0
1

vibravox-phonemes-tokenizer

license:mit
0
1

EBEN_noisy_soft_in_ear_microphone

license:mit
0
1