# Audio Classification Models, Page 2: Open-Weight AI Source: https://savrn.com/models/tasks/audio-classification/page/2 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub · Models by Task # Audio Classification Models 130 open-weight audio classification models in the SAVRN Model Hub, with Quan, AI at Meta and Superb publishing the most. 130Models 81Publishers 309K to 1.5BParameter range 8Licenses ## SAVRN's Take Audio classification covers everything from tagging the sounds in a clip to judging a speaker's emotion, age or gender. The most downloaded model here, LAION's CLAP, works like CLIP for sound: describe a category in words and it scores audio against it, with no training. That flexibility is why it leads by a wide margin. Most of these models are small enough to run on a CPU, so hardware is rarely the constraint. Licensing and purpose are. A meaningful share of this category carries noncommercial terms, including audEERING's age, gender and emotion models, and inferring personal traits from a voice raises privacy questions in many jurisdictions whatever the license says. For commercial sound tagging, the Apache and BSD options, CLAP and MIT's Audio Spectrogram Transformer among them, are the clean starting points. SAVRN Research, 2026-09-18 ## Most Downloaded | Model | Publisher | Parameters | License | Monthly downloads | Cheapest GPUs at 16-bit | | --- | --- | --- | --- | --- | --- | | [clap-htsat-fused](https://savrn.com/models/clap-htsat-fused) | LAION eV | 154M | apache-2.0 | 7.4M | 1x MI300X, $1.85/hr | | [wav2vec2-large-robust-24-ft-age-gender](https://savrn.com/models/wav2vec2-large-robust-24-ft-age-gender) | audEERING GmbH | 318M | cc-by-nc-sa-4.0 | 2.3M | 1x MI300X, $1.85/hr | | [ast-finetuned-audioset-10-10-0.4593](https://savrn.com/models/ast-finetuned-audioset-10-10-0-4593) | Massachusetts Institute of Technology | 87M | bsd-3-clause | 1.3M | 1x MI300X, $1.85/hr | | [ced-gguf](https://savrn.com/models/ced-gguf) | Mudler | — | apache-2.0 | 1M | — | | [wav2vec2-large-robust-12-ft-emotion-msp-dim](https://savrn.com/models/wav2vec2-large-robust-12-ft-emotion-msp-dim) | audEERING GmbH | 165M | cc-by-nc-sa-4.0 | 804.8k | 1x MI300X, $1.85/hr | | [wav2vec2-large-xlsr-53-gender-recognition-librispeech](https://savrn.com/models/wav2vec2-large-xlsr-53-gender-recognition-librispeech) | Alef Iury Siqueira Ferreira | 316M | apache-2.0 | 780.7k | 1x MI300X, $1.85/hr | | [audiobox-aesthetics](https://savrn.com/models/audiobox-aesthetics) | AI at Meta | 104M | cc-by-4.0 | 635.2k | 1x MI300X, $1.85/hr | | [open-vakgyata](https://savrn.com/models/open-vakgyata) | OneCXi | 59M | cc-by-nc-4.0 | 500.7k | 1x MI300X, $1.85/hr | | [MuQ-large-msd-iter](https://savrn.com/models/muq-large-msd-iter) | MuQ | 333M | cc-by-nc-4.0 | 328.2k | 1x MI300X, $1.85/hr | | [hubert-large-speech-emotion-recognition-russian-dusha-finetuned](https://savrn.com/models/hubert-large-speech-emotion-recognition-russian-dusha-finetuned) | Phil | 316M | apache-2.0 | 218.3k | 1x MI300X, $1.85/hr | ## Licenses | License | Models | Commercial use | | --- | --- | --- | | [apache-2.0](https://savrn.com/models/licenses/apache-2-0) | 48 | Yes | | not stated | 46 | Not stated | | [mit](https://savrn.com/models/licenses/mit) | 11 | Yes | | [cc-by-nc-4.0](https://savrn.com/models/licenses/cc-by-nc-4-0) | 11 | Not without separate permission | | [cc-by-nc-sa-4.0](https://savrn.com/models/licenses/cc-by-nc-sa-4-0) | 4 | Not without separate permission | | [cc-by-4.0](https://savrn.com/models/licenses/cc-by-4-0) | 4 | Yes | | [bsd-3-clause](https://savrn.com/models/licenses/bsd-3-clause) | 4 | Yes | | [openrail](https://savrn.com/models/licenses/openrail) | 2 | Yes, with conditions | ## Who Publishes Them | Publisher | Models | | --- | --- | | [Quan](https://savrn.com/model-publishers/wq2012) | 14 | | [AI at Meta](https://savrn.com/model-publishers/facebook) | 6 | | [Superb](https://savrn.com/model-publishers/superb) | 4 | | [SpeechBrain](https://savrn.com/model-publishers/speechbrain) | 3 | | [Massachusetts Institute of Technology](https://savrn.com/model-publishers/mit) | 3 | | [Speech Team, Xiaomi MiLM Plus](https://savrn.com/model-publishers/mispeech) | 3 | ## All 130 Models, Page 2 of 3 Model · Audio classification ### [wav2vec2-random-tiny-classifier](https://savrn.com/models/wav2vec2-random-tiny-classifier) [Anton Lozhkov](https://savrn.com/model-publishers/anton-l) Open weights transformers [View model](https://savrn.com/models/wav2vec2-random-tiny-classifier) Model · Audio classification ### [voxturn](https://savrn.com/models/voxturn) [Yang Xiao](https://savrn.com/model-publishers/austinxiao) An open audio turn-taking model that answers several questions about the moment in one forward pass: has the user finished their turn, how soon will they go on, is this a backchannel or an interruption, is the sound speech at all. It hears the last 8 seconds of user audio (optionally with a transcript or the dialog so far), runs on one GPU in ~8 ms, and is measured on all 14 languages of eot-bench and on two-party conversations in TurnBench. ▶ Interactive demo and film: replay real user turns in six languages and watch Pipecat's default turn model cut the user off while voxturn waits for the end of the turn. Technical report (PDF): method, training data, ablations and the full evaluation.… Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/voxturn) Model · Audio classification ### [pyannote-speaker-diarization.axera](https://savrn.com/models/pyannote-speaker-diarization-axera) [ZY](https://savrn.com/model-publishers/hy-2012) pyannote/speaker-diarization-community-1（cc-by-4.0）说话人日志模型的 AX650N 量化部署包： 输入 16 kHz 单声道会议音频，输出 RTTM 说话人时序标签。 - 模型: 8 个 axmodel（U16 激活 / S8 权重） - 依赖: Python 仅 numpy + scipy + scikit-learn + soundfile + axengine；C++ 为预编译 aarch64 可执行文件 常用参数：--step 2.0（滑动步长，2.5 更快 +0~1.5 pp）、--num-speakers 0（0=自动估计）。 切换 NPU2 版：--model-dir modelsnpu2（runax650.sh 通过环境变量 MODELDIR 指定）。 - 输入: 16 kHz 单声道 wav；输出: RTTM（SPEAKER 1 SPEAKERXX ） - 流水线: sincnet 分割（im2col + MatMul）→ 4 层 BiLSTM（96 步 cell，FP32 状态跨块） → 说话人嵌入（ResNet34 + TSTP）→ PLDA/AHC/VBx 聚类 - InstanceNorm / TSTP 统计池化在 U16 量化域数值崩坏，放主机 FP32 计算 - 完整转换源码见 GitHub: pyannote-speaker-diarization.AXERA 指标表所用 AMI / AliMeeting / VoxConverse 三个测试集的下载链接、参考处理与评分脚本见 ¹… Open weights cc-by-4.0 [View model](https://savrn.com/models/pyannote-speaker-diarization-axera) Model · Audio classification ### [ls_conformer_small_ge2e](https://savrn.com/models/ls-conformer-small-ge2e) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) - modelfp32.tflite: Exported FP32 TensorFlow Lite model for edge/mobile CPU inference - modelint8.tflite: Dynamic-range INT8 quantized TensorFlow Lite model (~3.5x smaller) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-small-ge2e) Model · Audio classification ### [ls_ecapa_tdnn_small_ge2e](https://savrn.com/models/ls-ecapa-tdnn-small-ge2e) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) - modelfp32.tflite: Exported FP32 TensorFlow Lite model for edge/mobile CPU inference - modelint8.tflite: Dynamic-range INT8 quantized TensorFlow Lite model (~3.5x smaller) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-ecapa-tdnn-small-ge2e) Model · Audio classification ### [beewatch_ast_3class](https://savrn.com/models/beewatch-ast-3class) [Jethro Tababa](https://savrn.com/model-publishers/troyskie) This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupsteps: 10 - numepochs: 4 - mixedprecisiontraining: Native AMP - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1 Open weights bsd-3-clause 86M parameters transformers [View model](https://savrn.com/models/beewatch-ast-3class) Model · Audio classification ### [cn_conformer_small_ge2e](https://savrn.com/models/cn-conformer-small-ge2e) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/cn-conformer-small-ge2e) Model · Audio classification ### [ls_conformer_small_arcface](https://savrn.com/models/ls-conformer-small-arcface) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-small-arcface) Model · Audio classification ### [cn_conformer_small_dr_vectors](https://savrn.com/models/cn-conformer-small-dr-vectors) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/cn-conformer-small-dr-vectors) Model · Audio classification ### [cn_ecapa_tdnn_small_arcface](https://savrn.com/models/cn-ecapa-tdnn-small-arcface) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/cn-ecapa-tdnn-small-arcface) Model · Audio classification ### [ls_conformer_small_cosface](https://savrn.com/models/ls-conformer-small-cosface) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-small-cosface) Model · Audio classification ### [ls_conformer_small_dr_vectors](https://savrn.com/models/ls-conformer-small-dr-vectors) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-small-dr-vectors) Model · Audio classification ### [ls_conformer_tiny_ge2e](https://savrn.com/models/ls-conformer-tiny-ge2e) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 693,124 parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-tiny-ge2e) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-5) [Kim Yeoungdoo](https://savrn.com/model-publishers/kud1223) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-5) Model · Audio classification ### [ls_conformer_small_triplet](https://savrn.com/models/ls-conformer-small-triplet) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-small-triplet) Model · Audio classification ### [ls_ecapa_tdnn_base_ge2e](https://savrn.com/models/ls-ecapa-tdnn-base-ge2e) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 3M parameters flaxspeaker [View model](https://savrn.com/models/ls-ecapa-tdnn-base-ge2e) Model · Audio classification ### [ls_conformer_small_ext_softmax](https://savrn.com/models/ls-conformer-small-ext-softmax) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-small-ext-softmax) Model · Audio classification ### [ls_conformer_base_ge2e](https://savrn.com/models/ls-conformer-base-ge2e) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 5M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-base-ge2e) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-12) [Yim](https://savrn.com/model-publishers/bbangju) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-12) Model · Audio classification ### [distilhubert-finetuned-gtzan](https://savrn.com/models/distilhubert-finetuned-gtzan) [Rajesh Kumar](https://savrn.com/model-publishers/imrajeshkr) This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set: - evalloss: 0.8970 - evalmodelpreparationtime: 0.0018 - evalaccuracy: 0.87 - evalruntime: 443.4759 - evalsamplespersecond: 0.225 - evalstepspersecond: 0.029 The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - lrschedulerwarmupsteps: 100 - numepochs: 10 - mixedprecisiontraining: Native AMP - labelsmoothingfactor: 0.1 - Transformers 5.16.1 - Pytorch 2.11.0+cpu - Datasets 2.19.0 - Tokenizers 0.23.1 Open weights apache-2.0 24M parameters transformers [View model](https://savrn.com/models/distilhubert-finetuned-gtzan) Model · Audio classification ### [ls_conformer_small_ge2e_contrast](https://savrn.com/models/ls-conformer-small-ge2e-contrast) [Quan](https://savrn.com/model-publishers/wq2012) model.safetensors: Primary model weights in SafeTensors format - flaxmodel.msgpack: Native Flax Msgpack serialized parameters - config.json: FlaxSpeakerConfig model & training architecture configuration - preprocessorconfig.json: WhisperFeatureExtractor-compatible frontend configuration - evalresults.json: Verified multi-domain evaluation metrics (1-utt & 3-utt EER, minDCF, ROC-AUC, 95% bootstrap CIs) Open weights apache-2.0 2M parameters flaxspeaker [View model](https://savrn.com/models/ls-conformer-small-ge2e-contrast) Model · Audio classification ### [arc-v1.4-alpha](https://savrn.com/models/arc-v1-4-alpha) [WhissleAI](https://savrn.com/model-publishers/whissleai) Open weights apache-2.0 22M parameters pytorch [View model](https://savrn.com/models/arc-v1-4-alpha) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls) [Rohjihye](https://savrn.com/model-publishers/kumkang) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-10) [Son Eunyoung](https://savrn.com/model-publishers/seu0514) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-10) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016) [Kyomins](https://savrn.com/model-publishers/kyomins) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-10) [Chae](https://savrn.com/model-publishers/hyos1517) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-10) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-11) [Heado Hwang](https://savrn.com/model-publishers/heado) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-11) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-2) [Ryusunghwan](https://savrn.com/model-publishers/ryusunghwan) This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 316M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-2) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-3) [Newhuman333](https://savrn.com/model-publishers/newhuman333) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - gradientaccumulationsteps: 8 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-3) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-4) [Ju ung](https://savrn.com/model-publishers/juran123) This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 316M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-4) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-5) [Mhj](https://savrn.com/model-publishers/phantomhj) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-5) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-6) [BEOM DAYEONG](https://savrn.com/model-publishers/beomdayeong) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-6) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-7) [Hong jin seong](https://savrn.com/model-publishers/hongjinseon123) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-7) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-8) [Kimteemo](https://savrn.com/model-publishers/kimteemo) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-8) Model · Audio classification ### [audio_cls_1016](https://savrn.com/models/audio-cls-1016-9) [Yuseung](https://savrn.com/model-publishers/youthpark) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-1016-9) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-11) [Bomi Kim](https://savrn.com/model-publishers/bornvom) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-11) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-13) [Eunjoolee](https://savrn.com/model-publishers/lara812) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-13) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-14) [Bbwwhh](https://savrn.com/model-publishers/bbwwhh) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-14) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-15) [Tkddjs45006](https://savrn.com/model-publishers/tkddjs45006) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-15) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-16) [GEUNYOUNG PARK](https://savrn.com/model-publishers/pgst89) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-16) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-2) [Kimyeongyu](https://savrn.com/model-publishers/kimyeongyu) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: - evalloss: 2.6565 - evalaccuracy: 0.0756 - evalruntime: 13.3085 - evalsamplespersecond: 8.942 - evalstepspersecond: 2.254 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-2) Model · Audio classification ### [audio_cls_261016](https://savrn.com/models/audio-cls-261016) [Park Gi Tae](https://savrn.com/model-publishers/pgt30245) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-261016) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-3) [Dohyeon](https://savrn.com/model-publishers/dohyeonhong) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-3) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-4) [Jaehyeong](https://savrn.com/model-publishers/rnlduqwb) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-4) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-6) [PARK POKKYU](https://savrn.com/model-publishers/p2kkyu) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-6) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-7) [YOONHYOENWOO](https://savrn.com/model-publishers/hyoenyee) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-7) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-8) [Ksmin](https://savrn.com/model-publishers/dieyoung0101) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-8) Model · Audio classification ### [audio_cls](https://savrn.com/models/audio-cls-9) [Kowntaeyang](https://savrn.com/model-publishers/getupsun) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-9) Model · Audio classification ### [audio_cls_hhd](https://savrn.com/models/audio-cls-hhd) [JeongDongWon](https://savrn.com/model-publishers/daemori) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-hhd) Model · Audio classification ### [audio_cls_ljg](https://savrn.com/models/audio-cls-ljg) [Kouikaya](https://savrn.com/model-publishers/kouikaya) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/audio-cls-ljg) Model · Audio classification ### [campplus](https://savrn.com/models/campplus) [losCrossos](https://savrn.com/model-publishers/loscrossos) This model is part of the FunASR ecosystem — one industrial-grade open-source / event · LLM-ASR. A Star really helps the project (and keeps you updated): Speaker Verification & Diarization — identify and distinguish speakers in audio. CAM++ is a speaker embedding model used for speaker verification (is this the same person?) and speaker diarization (who spoke when?). - Works with FunASR pipeline via spkmodel parameter Open weights apache-2.0 funasr [View model](https://savrn.com/models/campplus) Model · Audio classification ### [dsn-lid-whisper-base-1021-0007](https://savrn.com/models/dsn-lid-whisper-base-1021-0007) [Mustopha Hammed](https://savrn.com/model-publishers/mustyyyy) Open weights 21M parameters [View model](https://savrn.com/models/dsn-lid-whisper-base-1021-0007) Model · Audio classification ### [dsn-lid-whisper-base-trainval-1021-0007](https://savrn.com/models/dsn-lid-whisper-base-trainval-1021-0007) [Mustopha Hammed](https://savrn.com/model-publishers/mustyyyy) Open weights 21M parameters [View model](https://savrn.com/models/dsn-lid-whisper-base-trainval-1021-0007) Model · Audio classification ### [finetuned_checkpoints](https://savrn.com/models/finetuned-checkpoints) [TREA ORCA](https://savrn.com/model-publishers/trea-orca) This repository contains LoRA adapters produced by the TREA-ORCA audio question-answering fine-tuning runs. The available model names are af3, gemma4, and qwen2.5omni. The formats are mcq and opentext. Each task directory contains the final adapter checkpoint files: - adaptermodel.safetensors - adapterconfig.json - trainingargs.bin - trainerstate.json These are PEFT LoRA adapters, not standalone merged models. Load the matching base model first, then attach the adapter from the desired task directory with PEFT. MCQ adapters were trained with question text, four options, and a target option letter. The source data uses the question, optionA, optionB, optionC, optionD, and correct fields.… Open weights apache-2.0 transformers [View model](https://savrn.com/models/finetuned-checkpoints) Model · Audio classification ### [firebot-voice-intent](https://savrn.com/models/firebot-voice-intent) [Baena](https://savrn.com/model-publishers/anabaena) Official model checkpoints and speaker verification voiceprints for Firebot, an autonomous tactical firefighting robot platform with dual-tier offline voice intent recognition and personalized acoustic adaptation. The Firebot voice system is engineered for zero-latency, high-reliability command recognition in noisy operational environments. It combines: 1. Frozen Whisper Encoder Feature Extraction: 384-dimensional pooled audio embeddings from OpenAI's Whisper model (tiny.en / base.en). 2. L2-SP Regularized MLP Intent Classifier: A lightweight Multi-Layer Perceptron (LayerNorm $\to$ Linear 384$\times$128 $\to$ GELU $\to$ Dropout $\to$ Linear 128$\times$15) fine-tuned with an L2 penalty… Open weights mit pytorch [View model](https://savrn.com/models/firebot-voice-intent) Model · Audio classification ### [keyword](https://savrn.com/models/keyword) [PARKHYEIN](https://savrn.com/model-publishers/binfool) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/keyword) Model · Audio classification ### [keyword](https://savrn.com/models/keyword-10) [Yim](https://savrn.com/model-publishers/bbangju) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 4 - evalbatchsize: 4 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/keyword-10) Model · Audio classification ### [keyword](https://savrn.com/models/keyword-11) [AnnchaeJeong](https://savrn.com/model-publishers/mumi-gosomi) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/keyword-11) Model · Audio classification ### [keyword](https://savrn.com/models/keyword-12) [Kowntaeyang](https://savrn.com/model-publishers/getupsun) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/keyword-12) Model · Audio classification ### [keyword](https://savrn.com/models/keyword-13) [Ksmin](https://savrn.com/model-publishers/dieyoung0101) This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set: - evalloss: 0.9620 - evalaccuracy: 0.9474 - evalruntime: 18.9185 - evalsamplespersecond: 1.004 - evalstepspersecond: 0.159 The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - mixedprecisiontraining: Native AMP - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights 95M parameters transformers [View model](https://savrn.com/models/keyword-13) ## Questions ### Which Audio classification models are most downloaded? By monthly downloads reported by the Hugging Face Hub: wav2vec2-random-tiny-classifier (5.7k); voxturn (39); pyannote-speaker-diarization.axera (35). ## Other Tasks - [Text Generation](https://savrn.com/models/tasks/text-generation) 1506 - [Image and Text to Text](https://savrn.com/models/tasks/image-and-text-to-text) 560 - [Feature Extraction](https://savrn.com/models/tasks/feature-extraction) 396 - [Robotics](https://savrn.com/models/tasks/robotics) 379 - [Text Classification](https://savrn.com/models/tasks/text-classification) 328 - [Text to Image](https://savrn.com/models/tasks/text-to-image) 228 - [Speech Recognition](https://savrn.com/models/tasks/speech-recognition) 185 - [Text to Speech](https://savrn.com/models/tasks/text-to-speech) 143 - [Image Classification](https://savrn.com/models/tasks/image-classification) 139 - [Sentence Similarity](https://savrn.com/models/tasks/sentence-similarity) 103 - [Token Classification](https://savrn.com/models/tasks/token-classification) 99 - [Reinforcement Learning](https://savrn.com/models/tasks/reinforcement-learning) 97 [See all](https://savrn.com/models/tasks)