asr-crdnn-rnnlm-librispeech
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Explore AI models 27
asr-transformer-transformerlm-librispeech
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗cnn14-esc50
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗emotion-recognition-wav2vec2-IEMOCAP
speechbrainThis repository provides all the necessary tools to perform emotion recognition with a fine-tuned wav2vec2 (base) model using SpeechBrain. It is trained on IEMOCAP training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on IEMOCAP test set is: This system is composed of an wav2vec2 model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
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View details ↗google_speech_command_xvector
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗lang-id-voxlingua107-ecapa
speechbrainThis is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. We observed that this improved the performance of extracted utterance embeddings for downstream tasks. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese).
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View details ↗m-ctc-t-large
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗metricgan-plus-voicebank
speechbrainThis repository provides all the necessary tools to perform enhancement with SpeechBrain. For a better experience we encourage you to learn more about SpeechBrain. The model performance is: The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *enhance_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *enhance_batch* as in the example. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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View details ↗mtl-mimic-voicebank
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sepformer-dns4-16k-enhancement
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sepformer-libri2mix
speechbrainThis repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on Libri2Mix dataset. For a better experience we encourage you to learn more about SpeechBrain. The model performance is 20.6 dB on the test set of Libri2Mix dataset. The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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View details ↗sepformer-libri3mix
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sepformer-wham16k-enhancement
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sepformer-whamr
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sepformer-whamr-enhancement
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sepformer-whamr16k
speechbrainThis repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAMR! dataset with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 16k. For a better experience we encourage you to learn more about SpeechBrain. The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset. The system expects input recordings sampled at 16kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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View details ↗sepformer-wsj02mix
speechbrainThis repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset. For a better experience we encourage you to learn more about SpeechBrain. The model performance is 22.4 dB on the test set of WSJ0-2Mix dataset. The system expects input recordings sampled at 8kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.
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View details ↗sepformer-wsj03mix
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗soundchoice-g2p
speechbrain基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗spkrec-ecapa-voxceleb
speechbrainThis repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 1+ Voxceleb2 training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on Voxceleb1-test set(Cleaned) is: This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
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View details ↗spkrec-resnet-voxceleb
speechbrainThis repository provides all the necessary tools to perform speaker verification with a pretrained ResNet TDNN model using SpeechBrain. The system can be used to extract speaker embeddings as well. It is trained on Voxceleb 1 + Voxceleb2 training data. For a better experience, we encourage you to learn more about SpeechBrain. The model performance on Voxceleb1-test set(Cleaned) is: This system is composed of an ResNet TDNN model. The system is trained with Additive Margin Softmax Loss. Speaker Verification is performed using cosine distance between speaker embeddings.
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View details ↗spkrec-xvect-voxceleb
speechbrainThis repository provides all the necessary tools to extract speaker embeddings with a pretrained TDNN model using SpeechBrain. The system is trained on Voxceleb 1+ Voxceleb2 training data. For a better experience, we encourage you to learn more about SpeechBrain. The given model performance on Voxceleb1-test set (Cleaned) is: This system is composed of a TDNN model coupled with statistical pooling. The system is trained with Categorical Cross-Entropy Loss.
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View details ↗tts-hifigan-libritts-22050Hz
speechbrainThis repository provides all the necessary tools for using a HiFIGAN vocoder trained with LibriTTS (with multiple speakers). The sample rate used for the vocoder is 22050 Hz. The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram. Alternatives to this models are the following: - tts-hifigan-libritts-16kHz (same model trained on the same dataset, but for a sample rate of 16000 Hz) - tts-hifigan-ljspeech (same model trained on LJSpeech for a sample rate of 22050 Hz).
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