# wav2vec2-lg-xlsr-en-speech-emotion-recognition by Enrique Source: https://savrn.com/models/wav2vec2-lg-xlsr-en-speech-emotion-recognition Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve wav2vec2-lg-xlsr-en-speech-emotion-recognition (316M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.6 GB | 0.8 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.3 GB | 0.4 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.2 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 22, 2026. [wav2vec2-lg-xlsr-en-speech-emotion-recognition on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/wav2vec2-lg-xlsr-en-speech-emotion-recognition/gpus) ## Model Card By Enrique Hernández Calabrés, published under apache-2.0, revision b520c9c46a71. ### Speech Emotion Recognition By Fine-Tuning Wav2Vec 2.0 The model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) for a Speech Emotion Recognition (SER) task. The dataset used to fine-tune the original pre-trained model is the [RAVDESS dataset](https://zenodo.org/record/1188976#.YO6yI-gzaUk). This dataset provides 1440 samples of recordings from actors performing on 8 different emotions in English, which are: ``` emotions = ['angry', 'calm', 'disgust', 'fearful', 'happy', 'neutral', 'sad', 'surprised'] ``` It achieves the following results on the evaluation set: - Loss: 0.5023 - Accuracy: 0.8223 ### Model description More information needed ### Intended uses & limitations More information needed ### Training and evaluation data More information needed ### Training procedure #### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP #### Training results [Read the full model card (278 words)](https://savrn.com/models/wav2vec2-lg-xlsr-en-speech-emotion-recognition/card) ## Configuration Architecture Wav2Vec2ForSequenceClassification Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 Vocabulary size 33 Model type wav2vec2 ## Identity and Version Repository ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition Publisher Enrique Hernández Calabrés Task Audio classification Modality Audio Library transformers Parameters 316M parameters Languages Not stated by the source Revision b520c9c46a719e36e1b9a91cad2cb5d0668757d8 First published 2022-03-02 Last updated 2024-10-24 ## Files and Weights 14 files, 2.5 GB in total. The weights are 3 files totalling 2.5 GB in bin, safetensors. Weights3 files · 2.5 GB Configuration2 files · 2.5 KB Documentation1 file · 3.0 KB Other6 files · 42.4 KB Repository2 files · 804 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 1.3 GB | 33bd858e5a4d | | pytorch_model.bin | Weights | 1.3 GB | 51f2f61d0b1e | | training_args.bin | Weights | 2.9 KB | 73d0e655c82c | | config.json | Configuration | 2.3 KB | — | | preprocessor_config.json | Configuration | 214 B | — | | README.md | Documentation | 3.0 KB | — | | runs/Jul14_08-52-02_ea6be2bf8cd5/1626253006.9531522/events.out.tfevents.1626253006.ea6be2bf8cd5.900.5 | Other | 4.6 KB | 460b2ae294cc | | runs/Jul14_08-52-02_ea6be2bf8cd5/events.out.tfevents.1626253006.ea6be2bf8cd5.900.4 | Other | 5.0 KB | af1accc9a006 | | runs/Jul14_08-58-15_ea6be2bf8cd5/1626253103.0537474/events.out.tfevents.1626253103.ea6be2bf8cd5.1946.1 | Other | 4.6 KB | 369f6ff7d317 | | runs/Jul14_08-58-15_ea6be2bf8cd5/events.out.tfevents.1626253103.ea6be2bf8cd5.1946.0 | Other | 11.9 KB | 9350f7355b22 | | runs/Sep21_17-17-21_cc179d296121/1632245116.2700055/events.out.tfevents.1632245116.cc179d296121.76.1 | Other | 4.5 KB | bbb6d183f8cb | | runs/Sep21_17-17-21_cc179d296121/events.out.tfevents.1632245116.cc179d296121.76.0 | Other | 11.9 KB | fdd4dc2a0c25 | | .gitattributes | Repository | 791 B | — | | .gitignore | Repository | 13 B | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 2.5 GB [Download from Enrique Hernández Calabrés](https://huggingface.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition) Released by Enrique Hernández Calabrés through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 2.5 GB | | 16-bit | 0.6 GB | | 8-bit | 0.3 GB | | 4-bit | 0.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare wav2vec2-lg-xlsr-en-speech-emotion-recognition - [wav2vec2-large-robust-24-ft-age-gender vs wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://savrn.com/models/compare/wav2vec2-large-robust-24-ft-age-gender-vs-wav2vec2-lg-xlsr-en-speech-emotion-recognition) ## Questions About wav2vec2-lg-xlsr-en-speech-emotion-recognition ### How much GPU memory does wav2vec2-lg-xlsr-en-speech-emotion-recognition need? About 0.8 GB at 16-bit and 0.2 GB at 4-bit: the weights (316M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run wav2vec2-lg-xlsr-en-speech-emotion-recognition on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use wav2vec2-lg-xlsr-en-speech-emotion-recognition commercially? Yes. wav2vec2-lg-xlsr-en-speech-emotion-recognition is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Similar Models Model · Audio classification ### [wav2vec2-xlsr-53-russian-emotion-recognition](https://savrn.com/models/wav2vec2-xlsr-53-russian-emotion-recognition) [Aniemore](https://savrn.com/model-publishers/aniemore) Speech emotion recognition for Russian over seven classes: anger, disgust, enthusiasm, fear, happiness, neutral, sadness. Fine-tuned from jonatasgrosman/wav2vec2-large-xlsr-53-russian on Aniemore/resd. Audio resampled to 16 kHz mono, clips capped at 12 s, normalized per utterance, padding masked. UA is macro-averaged recall, WA is accuracy, F1 is macro-averaged. All three test sets went through the same harness, so the rows are comparable to each other. The RESD split matches fold 1 of EmoBox bit for bit. The top entry there is WavLM-large at WA 56.47 / UA 55.87 / F1 55.82. These numbers are higher, but the training protocol differs — EmoBox freezes the encoder and trains a probe, this is a… Open weights mit 316M parameters transformers [View model](https://savrn.com/models/wav2vec2-xlsr-53-russian-emotion-recognition) Model · Audio classification ### [wavlm-emotion-russian-resd](https://savrn.com/models/wavlm-emotion-russian-resd) [Aniemore](https://savrn.com/model-publishers/aniemore) Speech emotion recognition for Russian over seven classes: anger, disgust, enthusiasm, fear, happiness, neutral, sadness. Fine-tuned from jonatasgrosman/expw2v2truwavlms363 on Aniemore/resd. Audio resampled to 16 kHz mono, clips capped at 12 s, normalized per utterance, padding masked. UA is macro-averaged recall, WA is accuracy, F1 is macro-averaged. All three test sets went through the same harness, so the rows are comparable to each other. The RESD split matches fold 1 of EmoBox bit for bit. The top entry there is WavLM-large at WA 56.47 / UA 55.87 / F1 55.82. These numbers are higher, but the training protocol differs — EmoBox freezes the encoder and trains a probe, this is a full… Open weights mit 317M parameters transformers [View model](https://savrn.com/models/wavlm-emotion-russian-resd) 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-1016-4) [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 ### [hubert-large-speech-emotion-recognition-russian-dusha-finetuned](https://savrn.com/models/hubert-large-speech-emotion-recognition-russian-dusha-finetuned) [Phil](https://savrn.com/model-publishers/xbgoose) The pre-trained model is this one - facebook/hubert-large-ls960-ft The DUSHA dataset used can be found here Fine-tuned in Google Colab using Pro account with A100 GPU Freezed all layers exept projector, classifier and all 24 HubertEncoderLayerStableLayerNorm layers Used half of the train dataset - 2 epochs - train batch size = 8 - eval batch size = 8 - gradient accumulation steps = 4 - learning rate = 5e-5 without warm up and decay Achieved - accuracy = 0.86 - balanced = 0.76 - macro f1 score = 0.81 on test set, improving accucary and f1 score compared to dataset baseline Open weights apache-2.0 316M parameters transformers [View model](https://savrn.com/models/hubert-large-speech-emotion-recognition-russian-dusha-finetuned) Model · Audio classification ### [wav2vec2-large-xlsr-53-gender-recognition-librispeech](https://savrn.com/models/wav2vec2-large-xlsr-53-gender-recognition-librispeech) [Alef Iury Siqueira Ferreira](https://savrn.com/model-publishers/alefiury) This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on Librispeech-clean-100 for gender recognition. It achieves the following results on the evaluation set: The Librispeech-clean-100 dataset was used to train the model, with 70% of the data used for training, 10% for validation, and 20% for testing. The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 4 - evalbatchsize: 4 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 1 - mixedprecisiontraining: Native AMP - Transformers 4.28.0 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3 Open weights apache-2.0 316M parameters transformers [View model](https://savrn.com/models/wav2vec2-large-xlsr-53-gender-recognition-librispeech) ## Enrique Hernández Calabrés [All models and datasets](https://savrn.com/model-publishers/ehcalabres) ## Versions - [b520c9c46a71](https://savrn.com/models/wav2vec2-lg-xlsr-en-speech-emotion-recognition/versions/b520c9c46a71) · current 2026-09-18 ## Explore More - [All audio classification models](https://savrn.com/models/tasks/audio-classification) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-21. - [Hugging Face record](https://huggingface.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition) - [How the hub is built](https://savrn.com/model-hub/methodology)