183 lines
15 KiB
Markdown
183 lines
15 KiB
Markdown
# `data.py`
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This script handles the meat of preparing the data to feed the model through the dataloader, and unfortunately makes up for quite a lot of this project's complexity.
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Most of these settings live under `cfg.dataset`.
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## Dataset
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The provided reference model was trained on `?`k hours of audio with a mix of:
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* 490.151 hours (out of 585 hours) of LibriTTS-R's entire dataset
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* 8362.304 hours (out of 10270 hours) of `small`+`medium`+`duplicate` portions of LibriLight
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* 4467.611 hours (out of `?` hours) of Emilia's German, French, and Japanese dataset
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* 2927.186 hours (out of `?` hours) of a privately sourced corpus of 425 audiobooks
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* 2364.799 hours (out of `?` hours) of Emilia's English dataset
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* 54.775 hours of a personal small corpus of transcribed utterances from a selection of video games
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These durations were reported from the training script directly.
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* Utterances under 3 seconds or above 32 seconds were culled from the duration count.
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* Metadata was *mostly* derived from the transcription metadata, mostly.
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* LibriTTS-R's duration metadata was derived from the quantized audio size.
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### Leverage Your Own Dataset
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If you already have a dataset you want, for example, your own large corpus or for finetuning, you can use your own dataset instead.
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1. Populate your source voices under `./voices/{group name}/{speaker name}/`.
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2. Run `python3 -m vall_e.emb.transcribe`. This will generate a transcription with timestamps for your dataset.
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+ If you're interested in using a different model, edit the script's `model_name` and `batch_size` variables.
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3. Run `python3 -m vall_e.emb.process`. This will phonemize the transcriptions and quantize the audio.
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+ If you're using a Descript-Audio-Codec based model, ensure to set the sample rate and audio backend accordingly.
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4. Run `python3 -m vall_e.emb.similar`. This will calculate the top-k most similar utterances for each utterance for use with sampling.
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+ Doing this will help the model follow the input prompt stronger, at the possible "cost" of the model not learning how to "infer" the target speaker AND prosidy.
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5. Copy `./data/config.yaml` to `./training/config.yaml`. Customize the training configuration and populate your `dataset.training` list with the values stored under `./training/dataset/list.json`.
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+ Refer to `./vall_e/config.py` for additional configuration details.
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### Dataset Formats
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Two dataset formats are supported:
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* the standard way:
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- data is stored under `./training/data/{group}/{speaker}/{id}.{enc|dac}` as a NumPy file, where `enc` is for the EnCodec/Vocos backend, and `dac` for the Descript-Audio-Codec backend.
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- it is *highly* recommended to generate metadata to speed up dataset pre-load with `python3 -m vall_e.data --yaml="./training/config.yaml" --action=metadata`
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* using an HDF5 dataset:
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- you can convert from the standard way with the following command: `python3 -m vall_e.data --yaml="./training/config.yaml"` (metadata for dataset pre-load is generated alongside HDF5 creation)
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- this will shove everything into a single HDF5 file and store some metadata alongside (for now, the symbol map generated, and text/audio lengths)
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- be sure to also define `use_hdf5` in your config YAML.
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## Dataloader
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The dataloader handles some simple yet effective features, such as:
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* culling samples within a requested duration range
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* grouping samples based on:
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* speakers (to keep utterances for a given speaker) and groups (to keep similar speakers within a group as defined in the dataset)
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* durations, to keep VRAM usage and throughput consistent, if requested (as training requires keeping *all* samples of a batch the same token length)
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* further partitioning samples per GPU
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* shuffling then interleaving, per the dataloader sampler settings
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* saving/loading sampler states to disk
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* preparing a sample in a batch with adequate data for a given task, such as:
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* picking an input prompt similar to the sampled output audio, if requested
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* picking an input prompt from the same speaker as the sample, if the above is not requested
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* preparing the input sequence for the given task (such as non-TTS tasks)
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If `cfg.dataset.cache == True`, the initial list of paths and duration metadata (used for sorting/bucketing) is cached ~~through `diskcache`~~ under `{YAML_PATH}/.cache/{DATASET_HASH}/`. To allow for seamless modifications to the loaded dataset, the `DATASET_HASH` relies on:
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* duration range
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* folders/groups in the dataset
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* if using HDF5 (due to the key format differing)
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Be sure to delete the resultant `.cache` folder, as well as the `sampler.*` state dicts alongside checkpoints, if you plan to modify the dataloader settings between training sessions.
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## Tasks
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As this handles preparing the data fed into the model for training, this script needs to be aware of what tasks it should attend to, as mostly outlined under SpeechX.
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This section may be covered elsewhere in the documentation, but coverage here should focus on the specifics of attending to the task, rather than what the task is.
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* `tts`: basic and naive text-to-speech.
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* requires a text transcription, input audio prompt, and the output audio response.
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* `tts-c`: also noted as "VALL-E Continuous"
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* this is what most other TTS solutions abide by (those that require a transcription of the input prompt)
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* this *should* be more accurate as it has the output adhere stronger to the input through guidance, but doesn't seem to be necessary (to train for or inference under).
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* naively, this requires just the text transcription and output audio response, where part of the output audio response is trimmed to serve as the input audio prompt.
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* non-naively, this requires two text transcriptions, and two output audio responses (where one of them serve as the input audio prompt).
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* `stt`: basic and naive speech-to-text.
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* requires an input audio prompt and the output text transcription (as phonemes, unfortunately).
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* `ns`: noise suppression.
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* requires just a text transcription and an output audio response, where the input audio prompt is just the output + noise
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* text transcription can optionally be removed to allow for training without text guidance.
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* `sr`: speech removal.
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* requires just a text transcription and an output audio response, where the input audio prompt is just the sampled utterance + noise, and the output is just the original noise.
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* text transcription can optionally be removed to allow for training without text guidance.
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* `tse`: target speech extraction.
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* requires a text transcription, an input audio prompt of the sampled speaker, utterance sampled from a different speaker, and the output audio response.
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* the input prompt is appended with both the output audio and the utterance sampled from a different speaker overlayed on one another.
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* `cse`: clean speech editing.
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* an ideal world would have phoneme-level transcriptions, but I do not have very-accurate phoneme-level transcriptions.
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* to make up for this, this requires multiple samples for the prefix, the original middle, the edited portion for the middle, and the postfix sample.
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* the prefix and postfix *can* be randomly omitted, but keeping them in ensures better editing of speech within the middle.
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* requires four full samples.
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* `nse`: noisy speech editing.
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* the above, but injects some noise throughout the sampled utterances.
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A mystical `vc` for performing voice conversion is possible, but either requires a dataset to do so, or abusing an emergent property.
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* This emergent property is mostly abused through the NAR-len's demasking routine.
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## `__main__`
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This script can be called directly to perform dataloader-related tasks.
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### `--action=metadata`
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Invoking this will take processed samples (`.enc` for EnCodec, `.dac` for Descript-Audio-Codec) from `{YAML_PATH}/data/`, as per the YAML's `cfg.dataset.{training|validation|noise}` lists, and store helpful metadata under `{YAML_PATH}/metadata/`, to speed up dataloader preparations. Since dataloader preparations can cull based on audio durations, being able to look up a sample's duration speeds things up without needing to load the sample and read the file's metadata.
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This metadata can be then used to store similar speaker indices.
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### `--action=hdf5`
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Invoking this will take processed samples (`.enc` for EnCodec, `.dac` for Descript-Audio-Codec) from `{YAML_PATH}/data/`, as per the YAML's `cfg.dataset.{training|validation|noise}` lists, and store them within a single `.h5` HDF5 file.
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Additionally, this implicitly invokes `--action=metadata`, to create additional JSON metadata under `{YAML_PATH}/metadata/`, to speed up dataloader preparations.
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### `--action=sample`
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Invoking this will load the dataloader, sample it, and print out the batch's contents.
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This serves primarily for debugging purposes during development, and should not be necessary for the end user.
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### `--action=validate`
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Invoking this will process the dataset to check for any phonemes missing from the tokenizer (as defined under `cfg.tokenizer`).
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Any missing phonemes will be printed through `logger` to make mending the tokenizer dict easier.
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This serves primarily for debugging purposes during development, and should not be necessary for the end user. However, additional languages may emit additional IPAs through `phonemizer`, so those training additional languages should take care to validate for missing phonemes before training, to avoid headaches.
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## `cfg.dataset`
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This entry in the config YAML handles knobs and features related to the dataloader. This is defined as `Dataset` in `./vall_e/config.py`
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* `training`: list of entries to populate the training dataset with. Wildcards are accepted, such as `LibriVox/*` to easily load a speaker within a group, without needing to define them individually.
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* `validation`: the above, but for the validation dataset.
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* `noise`: the above, but for any noise that may be sampled during dataloader sampling. Text is not required for this dataset.
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* `speaker_name_getter`: a lambda function to evaluate, to retrieve the speaker name from a given path string.
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* `speaker_group_getter`: a lambda function to evaluate, to retrieve the speaker's associated group from a given path string.
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* `speaker_languages`: Deprecated. This is a dict that maps language codes to a list of speaker groups, for when the language code was not stored alongside a sample's data.
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* `use_hdf5`: use `{YAML_PATH}/{cfg.dataset.hdf5_name}` to sample data from, rather than individual files on disk.
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* `hdf5_name`: filename (or path?) to the HDF5 dataset file to load, if the above is requested.
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* `hdf5_flag`: flag to open the above HDF5 file under. By default this is `a` to write to, as it's necessary for HDF5 creation, but will automatically set to `r` under distributed settings.
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* `use_metadata`: references generated metadata instead of loading samples individually to acquire metadata.
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* `validate`: cull samples that do not fall within the requested `cfg.dataset.duration_range`.
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* `workers`: number of worker processes to handle dataloading under PyTorch.
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* `cache`: use diskcache when requested to not require subsequent processing. This handles *all* `diskcache` requests throughout the program if requested, but should only really be used under this script.
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* `min_utterances`: number of utterances to treat a speaker as valid.
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* `max_utterances`: maximum number of utterances a speaker can have. The remaining utterances are sliced off.
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* This is beneficial if you happen to have a section of your dataset with a ton of speakers, but you want to train on a plethora of speakers instead to balance out speaker.
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* `duration_range`: a list of two values to denote the acceptable duration ranges a sample is valid for the dataloader.
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* `sample_type`: type of sampler to use. Currently accepts `path` (an epoch is all paths in the dataset, and each index maps to each sample) or `speaker` (an epoch is all speakers in the dataset, and each index maps to each speaker)
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* `sample_order`: order to keep the dataloader sample. Currently accepts `interleaved` (tries to balance per speaker) and `duration` (orders by duration to keep throughput and VRAM usage consistent).
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* `sample_shuffle`: shuffles the dataloader sampler.
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* `sample_max_duration_batch`: the maximum total duration a batch can be. Values > 0 will enable batch sampling, where the dataloader sampler returns batches of batches.
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* This only works under `sample_order=duration` and `sample_type=path`, and should raise an exception for any other configuration.
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* `prompt_duration_range`: a list of two values to denote the range a sample's input prompt should be. This keeps the model trained for input prompt durations within these, and a little extra sometimes works without training for it.
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* `prompt_max_samples`: maximum number of utterances to sample for an input prompt to combine, if needed to fill the above duration window.
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* `prompt_continuous_utterance_p`: probability for a sample's input prompt to instead be the output prompt, and prepare the sample under "continuous" mode.
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* `prompt_similar_p`: probability to use a sample's most similar utterance as the input prompt, rather than randomly picking another utterance of the same speaker.
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* This requires adequate metadata to be available to store the top-K similar indices.
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* `prompt_similar_top_k`: use the top-k candidates for the above sampling.
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* `prompt_similar_top_k_offset`: the above, but an offset (as in it will not use the top-K-offset most similar utterances).
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* `prompt_inject_noise`: inject some noise in a sample's input prompt. *Will* harm dataloader throughput, as it requires re-encoding the audio.
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* `resps_max_samples`: maximum utterances to use for the sample's input text and output response audio.
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* `resps_append_p`: probability to append additional utterances to the sample.
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* `resps_pad_silence_p`: probability to pad the output response audio with silence. Does *not* require re-encoding, unless requested through `reencode_on_concat`.
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* `tasks_list`: list of task names a sample can be.
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* Currently supports: `tts`, `stt`, `tts-c`, `ns`, `sr`, `tse`, `nse`, `cse`
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* `reencode_on_concat`: if enabled, audio will be decoded to a raw waveform, concatted, then reencoded, instead of naively concatting EnCodec codes.
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* This isn't necessary naively concatting offers trivial inaccuracies.
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* `reencode_device`: device to load EnCodec within the dataloader.
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* *technically* only `cpu` should be supported, as loading models in dataloaders causes problems?
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* `noise_scale`: multiplier to the noise when applying noise. Lower numbers keep it quieter.
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* `retokenize_text`: if the text/phoneme transcription is available in the metadata, use that to re-tokenize instead of relying on the stored tokens itself.
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* This is helpful if you modify the tokenizer dict in post, but do not want to re-process the dataset to modify the tokenized phonemes.
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* `_frames_per_second`: overrides the internal tokens-per-second-of-audio ratio. Should never require modifying. |