diff --git a/_sources/recipes/TTS/ljspeech/vits.rst.txt b/_sources/recipes/TTS/ljspeech/vits.rst.txt index 323d0adfc8..d31bf63022 100644 --- a/_sources/recipes/TTS/ljspeech/vits.rst.txt +++ b/_sources/recipes/TTS/ljspeech/vits.rst.txt @@ -56,7 +56,8 @@ Training --start-epoch 1 \ --use-fp16 1 \ --exp-dir vits/exp \ - --tokens data/tokens.txt + --tokens data/tokens.txt \ + --model-type high \ --max-duration 500 .. note:: @@ -64,6 +65,11 @@ Training You can adjust the hyper-parameters to control the size of the VITS model and the training configurations. For more details, please run ``./vits/train.py --help``. +.. warning:: + + If you want a model that runs faster on CPU, please use ``--model-type low`` + or ``--model-type medium``. + .. note:: The training can take a long time (usually a couple of days). @@ -95,8 +101,8 @@ training part first. It will save the ground-truth and generated wavs to the dir Export models ------------- -Currently we only support ONNX model exporting. It will generate two files in the given ``exp-dir``: -``vits-epoch-*.onnx`` and ``vits-epoch-*.int8.onnx``. +Currently we only support ONNX model exporting. It will generate one file in the given ``exp-dir``: +``vits-epoch-*.onnx``. .. code-block:: bash @@ -120,4 +126,7 @@ Download pretrained models If you don't want to train from scratch, you can download the pretrained models by visiting the following link: - - ``_ + - ``--model-type=high``: ``_ + - ``--model-type=medium``: ``_ + - ``--model-type=low``: ``_ + diff --git a/recipes/TTS/ljspeech/vits.html b/recipes/TTS/ljspeech/vits.html index 2a9b4b63b0..9f792d98da 100644 --- a/recipes/TTS/ljspeech/vits.html +++ b/recipes/TTS/ljspeech/vits.html @@ -152,7 +152,8 @@

Training --start-epoch 1 \ --use-fp16 1 \ --exp-dir vits/exp \ - --tokens data/tokens.txt + --tokens data/tokens.txt \ + --model-type high \ --max-duration 500 @@ -161,6 +162,11 @@

Training./vits/train.py --help.

+
+

Warning

+

If you want a model that runs faster on CPU, please use --model-type low +or --model-type medium.

+

Note

The training can take a long time (usually a couple of days).

@@ -187,8 +193,8 @@

Inference

Export models

-

Currently we only support ONNX model exporting. It will generate two files in the given exp-dir: -vits-epoch-*.onnx and vits-epoch-*.int8.onnx.

+

Currently we only support ONNX model exporting. It will generate one file in the given exp-dir: +vits-epoch-*.onnx.

$ ./vits/export-onnx.py \
     --epoch 1000 \
     --exp-dir vits/exp \
@@ -208,7 +214,9 @@ 
Download pretrained models
-
  • https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2024-02-28
    • 
+
  • --model-type=high: https://huggingface.co/Zengwei/icefall-tts-ljspeech-vits-2024-02-28
    • 
+
  • --model-type=medium: https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-medium-2024-03-12
    • 
+
  • --model-type=low: https://huggingface.co/csukuangfj/icefall-tts-ljspeech-vits-low-2024-03-12
    •
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[12, "decoding"], [21, "decoding"], [33, "decoding"], [35, "decoding"], [36, "decoding"], [38, "decoding"], [39, "decoding"], [41, "decoding"], [42, "decoding"], [43, "decoding"], [44, "decoding"], [46, "decoding"], [47, "decoding"], [49, "decoding"], [55, "decoding"], [56, "decoding"], [57, "decoding"]], "Pre-trained model": [[2, "pre-trained-model"]], "Contributing": [[3, "contributing"]], "LODR for RNN Transducer": [[4, "lodr-for-rnn-transducer"]], "WER of LODR with different beam sizes": [[4, "id1"]], "Decoding with language models": [[5, "decoding-with-language-models"]], "LM rescoring for Transducer": [[6, "lm-rescoring-for-transducer"]], "WERs of LM rescoring with different beam sizes": [[6, "id1"]], "WERs of LM rescoring + LODR with different beam sizes": [[6, "id2"]], "LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)": [[6, "id3"]], "Shallow fusion for Transducer": [[7, "shallow-fusion-for-transducer"]], "WERs and decoding time (on test-clean) of shallow fusion with different beam sizes": [[7, "id2"]], "Docker": [[8, "docker"]], "Introduction": [[9, "introduction"], [53, "introduction"]], "View available tags": [[9, "view-available-tags"]], "CUDA-enabled docker images": [[9, "cuda-enabled-docker-images"]], "CPU-only docker images": [[9, "cpu-only-docker-images"]], "Download a docker image (CUDA)": [[9, "download-a-docker-image-cuda"]], "Download a docker image (CPU)": [[9, "download-a-docker-image-cpu"]], "Run a docker image with GPU": [[9, "run-a-docker-image-with-gpu"]], "Run a docker image with CPU": [[9, "run-a-docker-image-with-cpu"]], "Run yesno within a docker container": [[9, "run-yesno-within-a-docker-container"]], "Update the code": [[9, "update-the-code"]], "Data preparation": [[9, "data-preparation"], [21, "data-preparation"], [31, "data-preparation"], [33, "data-preparation"], [36, "data-preparation"], [38, "data-preparation"], [39, "data-preparation"], [41, "data-preparation"], [42, "data-preparation"], [43, "data-preparation"], [44, "data-preparation"], [46, "data-preparation"], [47, "data-preparation"], [49, "data-preparation"], [55, "data-preparation"], [56, "data-preparation"], [57, "data-preparation"], [59, "data-preparation"], [60, "data-preparation"]], "Frequently Asked Questions (FAQs)": [[10, "frequently-asked-questions-faqs"]], "OSError: libtorch_hip.so: cannot open shared object file: no such file or directory": [[10, "oserror-libtorch-hip-so-cannot-open-shared-object-file-no-such-file-or-directory"]], "AttributeError: module \u2018distutils\u2019 has no attribute \u2018version\u2019": [[10, "attributeerror-module-distutils-has-no-attribute-version"]], "ImportError: libpython3.10.so.1.0: cannot open shared object file: No such file or directory": [[10, "importerror-libpython3-10-so-1-0-cannot-open-shared-object-file-no-such-file-or-directory"]], "For the more curious": [[11, "for-the-more-curious"], [12, "for-the-more-curious"], [13, "for-the-more-curious"], [15, "for-the-more-curious"], [16, "for-the-more-curious"]], "A quick look to the generated files": [[11, "a-quick-look-to-the-generated-files"]], "download": [[11, "download"]], "data": [[11, "data"]], "Environment setup": [[13, "environment-setup"]], "Create a virtual environment": [[13, "create-a-virtual-environment"]], "Install dependencies": [[13, "install-dependencies"]], "Install icefall": [[13, "install-icefall"]], "Icefall for dummies tutorial": [[14, "icefall-for-dummies-tutorial"]], "Model Export": [[15, "model-export"]], "Export the model parameters via model.state_dict()": [[15, "export-the-model-parameters-via-model-state-dict"]], "Export via torch.jit.script()": [[15, "export-via-torch-jit-script"]], "Export via torch.onnx.export()": [[15, "export-via-torch-onnx-export"]], "Huggingface": [[17, "huggingface"]], "Pre-trained models": [[18, "pre-trained-models"]], "Huggingface spaces": [[19, "huggingface-spaces"]], "YouTube Video": [[19, "youtube-video"], [21, "youtube-video"]], "Icefall": [[20, "icefall"]], "Contents:": [[20, null]], "Installation": [[21, "installation"]], "(0) Install CUDA toolkit and cuDNN": [[21, "install-cuda-toolkit-and-cudnn"]], "(1) Install torch and torchaudio": [[21, "install-torch-and-torchaudio"]], "(2) Install k2": [[21, "install-k2"]], "(3) Install lhotse": [[21, "install-lhotse"]], "(4) Download icefall": [[21, "download-icefall"]], "Installation example": [[21, "installation-example"]], "(1) Create a virtual environment": [[21, "create-a-virtual-environment"]], "(2) Install CUDA toolkit and cuDNN": [[21, "id1"]], "(3) Install torch and torchaudio": [[21, "id2"]], "(4) Install k2": [[21, "id3"]], "(5) Install lhotse": [[21, "id5"]], "(6) Download icefall": [[21, "id6"]], "Test Your Installation": [[21, "test-your-installation"]], "Export model.state_dict()": [[22, "export-model-state-dict"], [41, "export-model-state-dict"], [43, "export-model-state-dict"], [44, "export-model-state-dict"], [55, "export-model-state-dict"], [56, "export-model-state-dict"], [57, "export-model-state-dict"]], "When to use it": [[22, "when-to-use-it"], [28, "when-to-use-it"], [29, "when-to-use-it"]], "How to export": [[22, "how-to-export"], [28, "how-to-export"], [29, "how-to-export"]], "How to use the exported model": [[22, "how-to-use-the-exported-model"], [28, "how-to-use-the-exported-model"]], "Use the exported model to run decode.py": [[22, "use-the-exported-model-to-run-decode-py"]], "Export to ncnn": [[23, "export-to-ncnn"]], "Export ConvEmformer transducer models to ncnn": [[24, "export-convemformer-transducer-models-to-ncnn"]], "1. Download the pre-trained model": [[24, "download-the-pre-trained-model"], [25, "download-the-pre-trained-model"], [26, "download-the-pre-trained-model"]], "2. Install ncnn and pnnx": [[24, "install-ncnn-and-pnnx"], [25, "install-ncnn-and-pnnx"], [26, "install-ncnn-and-pnnx"]], "3. Export the model via torch.jit.trace()": [[24, "export-the-model-via-torch-jit-trace"], [25, "export-the-model-via-torch-jit-trace"], [26, "export-the-model-via-torch-jit-trace"]], "4. Export torchscript model via pnnx": [[24, "export-torchscript-model-via-pnnx"], [25, "export-torchscript-model-via-pnnx"], [26, "export-torchscript-model-via-pnnx"]], "5. Test the exported models in icefall": [[24, "test-the-exported-models-in-icefall"], [25, "test-the-exported-models-in-icefall"], [26, "test-the-exported-models-in-icefall"]], "6. Modify the exported encoder for sherpa-ncnn": [[24, "modify-the-exported-encoder-for-sherpa-ncnn"], [25, "modify-the-exported-encoder-for-sherpa-ncnn"], [26, "modify-the-exported-encoder-for-sherpa-ncnn"]], "7. 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15, 22, 25, 26, 27, 35, 38, 39, 41, 42, 46, 47, 49, 53, 56, 57, 61], "encount": [4, 6, 7, 10, 21, 26, 33, 35, 36, 38, 39, 41, 43, 44, 55, 56, 57], "here": [4, 6, 7, 22, 24, 25, 26, 33, 35, 36, 38, 39, 42, 53, 56], "simplic": [4, 6, 7], "same": [4, 6, 7, 21, 22, 24, 25, 26, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 53, 55, 56, 57], "domain": [4, 6, 7, 31, 32], "gigaspeech": [4, 6, 7, 18, 28, 31, 55], "first": [4, 6, 9, 10, 11, 21, 24, 25, 26, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57, 59, 60], "let": [4, 6, 7, 14, 21, 24, 25, 26, 31, 33, 38, 51], "background": 4, "predecessor": 4, "dr": 4, "propos": [4, 35, 53, 57], "address": [4, 9, 15, 19, 21, 22, 24, 25, 26, 35, 41, 44, 55, 56, 57], "sourc": [4, 11, 13, 21, 22, 24, 25, 26, 33, 34, 35, 38], "acoust": [4, 56, 57], "similar": [4, 5, 31, 39, 43, 56, 57], "deriv": 4, "formula": 4, "bay": 4, "theorem": 4, "text": [4, 6, 7, 11, 16, 24, 25, 26, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57, 59, 60], "score": [4, 5, 7, 33, 38, 41, 56, 57], "left": [4, 24, 26, 35, 56, 57], "y_u": 4, "mathit": 4, "y": 4, "right": [4, 24, 35, 53, 56], "log": [4, 9, 10, 12, 15, 16, 21, 24, 25, 26, 42, 46, 47, 49, 59, 60], "y_": 4, "u": [4, 21, 24, 25, 26, 33, 35, 36, 38, 39, 49], "lambda_1": 4, "p_": 4, "lambda_2": 4, "where": [4, 9, 10, 56], "weight": [4, 15, 33, 36, 38, 43, 44, 51, 55], "respect": 4, "onli": [4, 6, 8, 11, 13, 14, 15, 22, 24, 25, 26, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 53, 55, 56, 57, 59, 60], "compar": [4, 24, 25, 26, 31, 56], "shallow": [4, 5, 20, 51], "fusion": [4, 5, 20, 51], "subtract": [4, 5], "work": [4, 9, 13, 15, 24, 25, 26, 38], "treat": [4, 25, 26], "predictor": 4, "joiner": [4, 24, 25, 26, 27, 29, 31, 35, 41, 55, 56, 57], "weak": 4, "captur": 4, "therefor": [4, 10], "n": [4, 5, 6, 11, 21, 33, 39, 41, 43, 44, 46, 47, 55, 56, 57], "gram": [4, 6, 21, 33, 35, 36, 41, 42, 44, 46, 47, 56, 57], "approxim": [4, 5], "ilm": 4, "lead": [4, 7, 12], "rnnt": [4, 41, 56, 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43, 44, 49, 51, 55, 56, 57], "download": [4, 6, 7, 8, 10, 13, 15, 19, 20, 23, 30, 31, 34, 39, 51, 58], "git_lfs_skip_smudg": [4, 6, 7, 24, 25, 26, 27, 31, 51], "huggingfac": [4, 6, 7, 18, 20, 21, 22, 24, 25, 26, 27, 31, 33, 35, 36, 38, 42, 43, 44, 46, 47, 49, 51, 55, 59, 60], "co": [4, 6, 7, 18, 19, 21, 22, 24, 25, 26, 27, 31, 33, 34, 35, 36, 38, 42, 43, 44, 46, 47, 49, 51, 55, 59, 60], "zengwei": [4, 6, 7, 24, 26, 27, 31, 44, 51, 55, 59], "stateless7": [4, 6, 7, 26, 27], "2022": [4, 6, 7, 22, 24, 25, 26, 27, 35, 41, 43, 44, 55, 56], "12": [4, 6, 7, 9, 14, 21, 22, 24, 25, 26, 27, 33, 35, 36, 38, 41, 43, 44, 46, 49, 55, 56, 57, 59, 60], "29": [4, 6, 7, 21, 26, 27, 33, 35, 36, 38, 42, 43, 46, 47], "exp": [4, 6, 7, 9, 15, 16, 21, 22, 24, 25, 26, 27, 28, 29, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57, 59, 60], "lf": [4, 6, 7, 22, 24, 25, 26, 27, 31, 33, 35, 36, 38, 42, 44, 46, 47, 49, 51], "includ": [4, 6, 7, 24, 25, 26, 27, 31, 41, 43, 44, 51, 55, 56, 57], "pt": [4, 6, 7, 9, 11, 15, 21, 22, 24, 25, 26, 27, 28, 29, 31, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57], "ln": [4, 6, 7, 9, 15, 22, 24, 25, 26, 27, 31, 33, 38, 41, 43, 44, 51, 55, 56, 57], "epoch": [4, 6, 7, 9, 12, 15, 16, 21, 22, 24, 25, 26, 27, 28, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57, 59, 60], "99": [4, 6, 7, 15, 21, 24, 25, 26, 27, 31], "symbol": [4, 5, 6, 7, 21, 35, 41, 56, 57], "load": [4, 6, 7, 9, 15, 21, 24, 25, 26, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57], "lang_bpe_500": [4, 6, 7, 22, 24, 25, 26, 27, 28, 29, 31, 38, 41, 43, 44, 51, 55, 56, 57], "bpe": [4, 5, 6, 7, 22, 24, 25, 26, 27, 29, 31, 38, 41, 43, 44, 51, 55, 56, 57], "done": [4, 6, 7, 9, 13, 15, 21, 22, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57], "via": [4, 6, 7, 14, 21, 23, 28, 29, 30, 31, 51], "exp_dir": [4, 6, 7, 9, 15, 21, 24, 25, 26, 35, 38, 39, 41, 43, 44, 56, 57], "avg": [4, 6, 7, 9, 12, 15, 21, 22, 24, 25, 26, 27, 28, 29, 31, 35, 38, 39, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57], "averag": [4, 6, 7, 9, 12, 15, 21, 22, 24, 25, 26, 27, 31, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57], "fals": [4, 6, 7, 9, 15, 21, 22, 24, 25, 26, 31, 33, 35, 38, 39], "dir": [4, 6, 7, 22, 24, 25, 26, 27, 28, 29, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57, 59, 60], "max": [4, 6, 7, 21, 22, 24, 25, 31, 33, 35, 36, 38, 39, 41, 43, 44, 55, 56, 57, 59, 60], "durat": [4, 6, 7, 11, 22, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57, 59, 60], "600": [4, 6, 7, 21, 22, 38, 41, 43, 55, 56, 57], "chunk": [4, 6, 7, 24, 26, 27, 56, 57], "len": [4, 6, 7, 26, 27, 57], "32": [4, 6, 7, 21, 24, 25, 26, 27, 33, 35, 36, 57], "method": [4, 5, 7, 15, 19, 22, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 55, 56, 57], "modified_beam_search": [4, 5, 6, 7, 19, 35, 39, 41, 43, 55, 56, 57], "clean": [4, 9, 15, 21, 26, 33, 35, 38, 39, 41, 42, 43, 44, 55, 56, 57], "beam_size_4": [4, 6, 7], "11": [4, 6, 7, 9, 10, 11, 15, 21, 24, 25, 27, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57], "best": [4, 5, 6, 7, 24, 25, 26, 31, 33, 36, 38], "7": [4, 6, 7, 9, 21, 22, 23, 26, 30, 33, 36, 38, 41, 42, 46, 47, 55, 56], "93": [4, 6, 7, 15], "Then": [4, 6], "necessari": [4, 39, 51], "note": [4, 5, 6, 7, 10, 11, 15, 22, 24, 31, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57], "960": [4, 31, 38, 41, 43, 44, 55, 56, 57], "hour": [4, 13, 31, 33, 35, 36, 38, 41, 43, 44, 55, 56, 57], "ezerhouni": [4, 6, 7], "pushd": [4, 6, 7, 27], "popd": [4, 6, 7, 27], "marcoyang": [4, 6], "librispeech_bigram": [4, 6], "2gram": [4, 6], "fst": [4, 11, 21, 35, 49], "modified_beam_search_lm_lodr": 4, "lm_dir": [4, 6, 7, 9, 21, 38], "lm_scale": [4, 6, 7], "42": [4, 9, 15, 21, 25, 33, 38, 49], "lodr_scal": 4, "24": [4, 9, 10, 13, 15, 21, 24, 25, 36, 42, 46, 47, 49], "modified_beam_search_lodr": [4, 5, 6], "scale": [4, 6, 7, 24, 25, 33, 38, 39, 42, 44, 46, 47], "embed": [4, 6, 7, 35, 41, 51, 55, 56, 57], "dim": [4, 6, 7, 24, 25, 26, 35, 41, 51, 56], "2048": [4, 6, 7, 22, 24, 25, 26, 35, 51], "hidden": [4, 6, 7, 25, 51, 55], "num": [4, 6, 7, 24, 25, 26, 31, 33, 35, 36, 38, 39, 41, 43, 44, 51, 55, 56, 57, 59, 60], "layer": [4, 6, 7, 24, 25, 26, 35, 39, 41, 51, 53, 55, 56, 57], "vocab": [4, 6, 7, 38], "500": [4, 6, 7, 22, 24, 25, 26, 35, 38, 44, 55, 59, 60], "token": [4, 11, 22, 24, 25, 26, 27, 28, 29, 33, 35, 36, 38, 42, 46, 47, 49, 51, 59, 60], "ngram": [4, 38, 42, 46, 47], "2": [4, 6, 7, 9, 11, 13, 15, 20, 22, 23, 30, 31, 39, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57, 59, 60], "extra": [4, 24, 25, 26, 35, 53, 56], "argument": [4, 7, 15, 31, 39, 53], "need": [4, 6, 11, 13, 14, 15, 19, 21, 22, 23, 24, 25, 26, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 51, 53, 55, 56, 57], "given": [4, 9, 11, 12, 13, 15, 21, 22, 24, 25, 26, 33, 35, 36, 38, 41, 42, 43, 44, 56, 57, 59, 60], "specifi": [4, 7, 10, 12, 15, 16, 24, 25, 26, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57], "neg": [4, 35], "number": [4, 7, 16, 19, 22, 24, 25, 26, 31, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 55, 56, 57], "obtain": [4, 7, 33, 35, 36, 38, 42, 46, 47], "shown": [4, 7], "below": [4, 7, 9, 11, 12, 13, 14, 15, 16, 21, 24, 25, 26, 31, 33, 35, 36, 38, 41, 42, 43, 44, 46, 47, 49, 55, 56], "61": [4, 6], "6": [4, 6, 7, 9, 10, 11, 15, 23, 30, 33, 35, 38, 41, 42, 46, 47, 55, 60], "74": [4, 6, 21, 22], "recal": 4, "lowest": [4, 12, 15, 41, 43, 44, 55, 56, 57], "77": [4, 6, 7, 21, 38], "08": [4, 6, 7, 9, 15, 26, 38, 42, 44, 46, 47, 49, 55], "inde": 4, "even": [4, 19, 21, 25], "better": [4, 6], "increas": [4, 6, 33, 35, 36, 38, 41, 43, 44, 55, 56, 57], "8": [4, 6, 7, 9, 10, 15, 21, 22, 24, 25, 26, 33, 35, 38, 39, 41, 42, 43, 44, 49, 55, 56, 57], "45": [4, 6, 15, 21, 24, 26, 33, 35, 38], "38": [4, 6, 21, 24, 33, 35, 38, 46], "23": [4, 6, 9, 10, 11, 15, 21, 24, 25, 26, 33, 35, 36, 38, 46, 47, 49], "section": [5, 8, 9, 10, 17, 21, 22, 27, 28, 29, 30, 33, 38], "langugag": 5, "transduc": [5, 20, 22, 23, 27, 30, 31, 34, 37, 39, 40, 51, 52, 53, 54], "rnn": [5, 6, 7, 20, 25, 35, 41, 43, 55, 56, 57, 61], "avail": [5, 6, 8, 15, 20, 21, 22, 24, 25, 26, 31, 32, 33, 35, 38, 42, 46, 47, 49, 55], "beam": [5, 22, 55], "search": [5, 6, 7, 18, 19, 58], "realli": [5, 33, 36, 38, 41, 43, 44, 55, 56, 57], "valu": [5, 7, 24, 25, 26, 31, 33, 35, 36, 38, 41, 43, 44, 55, 56, 57], "t": [5, 13, 14, 15, 21, 24, 25, 26, 27, 28, 31, 33, 35, 36, 38, 39, 41, 42, 43, 44, 46, 47, 49, 51, 55, 56, 57, 59, 60], "doe": [5, 15, 24, 25, 26, 33, 35, 38, 49], "modified_beam_search_lm_shallow_fus": [5, 6, 7], "interpol": 5, "also": [5, 6, 7, 11, 13, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 33, 35, 36, 38, 41, 43, 44, 49, 51, 53, 55, 56, 57], "known": 5, "bigram": 5, "backoff": 5, "modified_beam_search_lm_rescor": [5, 6], "hypothes": [5, 6], "rnnlm": [5, 6, 51], "re": [5, 6, 10, 33, 36, 38, 39, 41, 43, 44, 53, 55, 56, 57], "rank": [5, 6], "modified_beam_search_lm_rescore_lodr": [5, 6], "lodr": [5, 20, 51], 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the code style": [[0, "follow-the-code-style"]], "Contributing to Documentation": [[1, "contributing-to-documentation"]], "How to create a recipe": [[2, "how-to-create-a-recipe"]], "Data Preparation": [[2, "data-preparation"], [11, "data-preparation"], [35, "data-preparation"]], "Training": [[2, "training"], [9, "training"], [16, "training"], [21, "training"], [33, "training"], [35, "training"], [36, "training"], [38, "training"], [39, "training"], [41, "training"], [42, "training"], [43, "training"], [44, "training"], [46, "training"], [47, "training"], [49, "training"], [55, "training"], [56, "training"], [57, "training"], [59, "training"], [60, "training"]], "Decoding": [[2, "decoding"], [9, "decoding"], [12, "decoding"], [21, "decoding"], [33, "decoding"], [35, "decoding"], [36, "decoding"], [38, "decoding"], [39, "decoding"], [41, "decoding"], [42, "decoding"], [43, "decoding"], [44, "decoding"], [46, "decoding"], [47, "decoding"], [49, "decoding"], [55, "decoding"], [56, "decoding"], [57, "decoding"]], "Pre-trained model": [[2, "pre-trained-model"]], "Contributing": [[3, "contributing"]], "LODR for RNN Transducer": [[4, "lodr-for-rnn-transducer"]], "WER of LODR with different beam sizes": [[4, "id1"]], "Decoding with language models": [[5, "decoding-with-language-models"]], "LM rescoring for Transducer": [[6, "lm-rescoring-for-transducer"]], "WERs of LM rescoring with different beam sizes": [[6, "id1"]], "WERs of LM rescoring + LODR with different beam sizes": [[6, "id2"]], "LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)": [[6, "id3"]], "Shallow fusion for Transducer": [[7, "shallow-fusion-for-transducer"]], "WERs and decoding time (on test-clean) of shallow fusion with different beam sizes": [[7, "id2"]], "Docker": [[8, "docker"]], "Introduction": [[9, "introduction"], [53, "introduction"]], "View available tags": [[9, "view-available-tags"]], "CUDA-enabled docker images": [[9, "cuda-enabled-docker-images"]], "CPU-only docker images": [[9, "cpu-only-docker-images"]], "Download a docker image (CUDA)": [[9, "download-a-docker-image-cuda"]], "Download a docker image (CPU)": [[9, "download-a-docker-image-cpu"]], "Run a docker image with GPU": [[9, "run-a-docker-image-with-gpu"]], "Run a docker image with CPU": [[9, "run-a-docker-image-with-cpu"]], "Run yesno within a docker container": [[9, "run-yesno-within-a-docker-container"]], "Update the code": [[9, "update-the-code"]], "Data preparation": [[9, "data-preparation"], [21, "data-preparation"], [31, "data-preparation"], [33, "data-preparation"], [36, "data-preparation"], [38, "data-preparation"], [39, "data-preparation"], [41, "data-preparation"], [42, "data-preparation"], [43, "data-preparation"], [44, "data-preparation"], [46, "data-preparation"], [47, "data-preparation"], [49, "data-preparation"], [55, "data-preparation"], [56, "data-preparation"], [57, "data-preparation"], [59, "data-preparation"], [60, "data-preparation"]], "Frequently Asked Questions (FAQs)": [[10, "frequently-asked-questions-faqs"]], "OSError: libtorch_hip.so: cannot open shared object file: no such file or directory": [[10, "oserror-libtorch-hip-so-cannot-open-shared-object-file-no-such-file-or-directory"]], "AttributeError: module \u2018distutils\u2019 has no attribute \u2018version\u2019": [[10, "attributeerror-module-distutils-has-no-attribute-version"]], "ImportError: libpython3.10.so.1.0: cannot open shared object file: No such file or directory": [[10, "importerror-libpython3-10-so-1-0-cannot-open-shared-object-file-no-such-file-or-directory"]], "For the more curious": [[11, "for-the-more-curious"], [12, "for-the-more-curious"], [13, "for-the-more-curious"], [15, "for-the-more-curious"], [16, "for-the-more-curious"]], "A quick look to the generated files": [[11, "a-quick-look-to-the-generated-files"]], "download": [[11, "download"]], "data": [[11, "data"]], "Environment setup": [[13, "environment-setup"]], "Create a virtual environment": [[13, "create-a-virtual-environment"]], "Install dependencies": [[13, "install-dependencies"]], "Install icefall": [[13, "install-icefall"]], "Icefall for dummies tutorial": [[14, "icefall-for-dummies-tutorial"]], "Model Export": [[15, "model-export"]], "Export the model parameters via model.state_dict()": [[15, "export-the-model-parameters-via-model-state-dict"]], "Export via torch.jit.script()": [[15, "export-via-torch-jit-script"]], "Export via torch.onnx.export()": [[15, "export-via-torch-onnx-export"]], "Huggingface": [[17, "huggingface"]], "Pre-trained models": [[18, "pre-trained-models"]], "Huggingface spaces": [[19, "huggingface-spaces"]], "YouTube Video": [[19, "youtube-video"], [21, "youtube-video"]], "Icefall": [[20, "icefall"]], "Contents:": [[20, null]], "Installation": [[21, "installation"]], "(0) Install CUDA toolkit and cuDNN": [[21, "install-cuda-toolkit-and-cudnn"]], "(1) Install torch and torchaudio": [[21, "install-torch-and-torchaudio"]], "(2) Install k2": [[21, "install-k2"]], "(3) Install lhotse": [[21, "install-lhotse"]], "(4) Download icefall": [[21, "download-icefall"]], "Installation example": [[21, "installation-example"]], "(1) Create a virtual environment": [[21, "create-a-virtual-environment"]], "(2) Install CUDA toolkit and cuDNN": [[21, "id1"]], "(3) Install torch and torchaudio": [[21, "id2"]], "(4) Install k2": [[21, "id3"]], "(5) Install lhotse": [[21, "id5"]], "(6) Download icefall": [[21, "id6"]], "Test Your Installation": [[21, "test-your-installation"]], "Export model.state_dict()": [[22, "export-model-state-dict"], [41, "export-model-state-dict"], [43, "export-model-state-dict"], [44, "export-model-state-dict"], [55, "export-model-state-dict"], [56, "export-model-state-dict"], [57, "export-model-state-dict"]], "When to use it": [[22, "when-to-use-it"], [28, "when-to-use-it"], [29, "when-to-use-it"]], "How to export": [[22, "how-to-export"], [28, "how-to-export"], [29, "how-to-export"]], "How to use the exported model": [[22, "how-to-use-the-exported-model"], [28, "how-to-use-the-exported-model"]], "Use the exported model to run decode.py": [[22, "use-the-exported-model-to-run-decode-py"]], "Export to ncnn": [[23, "export-to-ncnn"]], "Export ConvEmformer transducer models to ncnn": [[24, "export-convemformer-transducer-models-to-ncnn"]], "1. 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