# Hyperion Toolkit Speaker Verification pre-trained Model

## Model Configuration
This model was trained using recipe [voxceleb/v1.1](https://github.com/hyperion-ml/hyperion/tree/master/egs/voxceleb/v1.1)

The configuration for this modeis is defined in
[config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.v1.sh](https://github.com/hyperion-ml/hyperion/blob/master/egs/voxceleb/v1.1/global_conf/config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.v1.sh)

This is an x-vector model with:
  - 80 logMel filter-banks with short-time mean normalization.
  - ThinResNet34 (aka Light ResNet34) encoder.
  - Mean+Stddev pooling
  - AAM-softmax loss (m=0.3, s=30)
  - Mixed prec. training.

{"language": ["en"], "license": "apache-2.0", "tags": ["hyperion", "audio", "speech", "speaker-recognition", "x-vector", "thin-resnet34"], "datasets": ["voxceleb"], "metrics": ["eer", "min_dcf-p=0.05", "min_dcf-p=0.01"], "model-index": [{"name": "voxceleb-v1.1-fbank80_stmn_lresnet34_e256_arcs30m0.3_do0_adam_lr0.05_b512.v1", "results": [{"task": {"type": "speaker-verification", "name": "Speaker Verification"}, "dataset": {"name": "Voxceleb1", "type": "voxceleb1", "args": "Train on VoxCeleb2-dev"}, "metrics": [{"type": "eer", "value": 2.11, "name": "EER Vox1-O"}, {"type": "min_dcf-p=0.05", "value": 0.135, "name": "Minimum DCF Vox1-O prior=0.05"}, {"type": "act_dcf-p=0.01", "value": 0.208, "name": "Minimum DCF Vox1-O prior=0.01"}, {"type": "eer", "value": 1.93, "name": "EER Vox1-E"}, {"type": "min_dcf-p=0.05", "value": 0.121, "name": "Minimum DCF Vox1-E prior=0.05"}, {"type": "act_dcf-p=0.01", "value": 0.204, "name": "Minimum DCF Vox1-E Original prior=0.01"}, {"type": "eer", "value": 3.21, "name": "EER Vox1-H"}, {"type": "min_dcf-p=0.05", "value": 0.19, "name": "Minimum DCF Vox1-H prior=0.05"}, {"type": "act_dcf-p=0.01", "value": 0.298, "name": "Minimum DCF Vox1-H Original prior=0.01"}]}]}]}
hyperion audio speech speaker-recognition x-vector thin-resnet34 en dataset:voxceleb license:apache-2.0 model-index region:us
TAGS
#hyperion #audio #speech #speaker-recognition #x-vector #thin-resnet34 #en #dataset-voxceleb #license-apache-2.0 #model-index #region-us 

# Hyperion Toolkit Speaker Verification pre-trained Model

## Model Configuration
This model was trained using recipe voxceleb/v1.1

The configuration for this modeis is defined in
config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.URL

This is an x-vector model with:
  - 80 logMel filter-banks with short-time mean normalization.
  - ThinResNet34 (aka Light ResNet34) encoder.
  - Mean+Stddev pooling
  - AAM-softmax loss (m=0.3, s=30)
  - Mixed prec. training.

# Hyperion Toolkit Speaker Verification pre-trained Model ## Model Configuration
This model was trained using recipe voxceleb/v1.1

The configuration for this modeis is defined in
config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.URL

This is an x-vector model with:
  - 80 logMel filter-banks with short-time mean normalization.
  - ThinResNet34 (aka Light ResNet34) encoder.
  - Mean+Stddev pooling
  - AAM-softmax loss (m=0.3, s=30)
  - Mixed prec. training.
TAGS
#hyperion #audio #speech #speaker-recognition #x-vector #thin-resnet34 #en #dataset-voxceleb #license-apache-2.0 #model-index #region-us 
 # Hyperion Toolkit Speaker Verification pre-trained Model ## Model Configuration
This model was trained using recipe voxceleb/v1.1

The configuration for this modeis is defined in
config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.URL

This is an x-vector model with:
  - 80 logMel filter-banks with short-time mean normalization.
  - ThinResNet34 (aka Light ResNet34) encoder.
  - Mean+Stddev pooling
  - AAM-softmax loss (m=0.3, s=30)
  - Mixed prec. training.
passage: TAGS
#hyperion #audio #speech #speaker-recognition #x-vector #thin-resnet34 #en #dataset-voxceleb #license-apache-2.0 #model-index #region-us 
# Hyperion Toolkit Speaker Verification pre-trained Model## Model Configuration
This model was trained using recipe voxceleb/v1.1

The configuration for this modeis is defined in
config_fbank80_stmn_lresnet34_arcs30m0.3_adam_lr0.05_amp.URL

This is an x-vector model with:
  - 80 logMel filter-banks with short-time mean normalization.
  - ThinResNet34 (aka Light ResNet34) encoder.
  - Mean+Stddev pooling
  - AAM-softmax loss (m=0.3, s=30)
  - Mixed prec. training.