DeepForcedAligner

Paths modified to train on ICASSP LIMMITS23 dataset. It is trained on each speaker separetly, by changing "metadata_path" to 6 _ combinations, eg: Hindi_M, Telugu_F, etc. You may train on all 6 speakers, with a multilingual setup also. For any questions, contact challenge.syspin@iisc.ac.in

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With this tool you can create accurate text-audio alignments given a bunch of audio files and their transcription. The alignments can for example be used to train text-to-speech models such as FastSpeech. In comparison to other forced alignment tools this repo has following advantages:

• Multilingual: By design, the DFA is language-agnostic and can align both characters or phonemes.
• Robustness: The alignment extraction is highly tolerant against text errors and silent characters.
• Convenience: Easy installation with no extra dependencies. You can provide your own data in the standard LJSpeech format without special preprocessing (such as applying phonetic dictionaries, non-speech annotations etc.).

The approach is based on training a simple speech recognition model with CTC loss on mel spectrograms extracted from the wav files.

Installation

Running on Python >=3.6

pip install -r requirements.txt

Example Training and Extraction

Check out the following demo notebook for training and character duration extraction on the LJSpeech dataset:

(1) Download the LJSpeech dataset, set paths in config.yaml:

  dataset_dir: LJSpeech
  metadata_path: LJSpeech/metadata.csv

(2) Preprocess the data and train aligner:

  python preprocess.py
  python train.py

(3) Extract durations with latest model checkpoint (60k steps should be sufficient):

  python extract_durations.py

By default durations are put as numpy files into:

  output/durations 

Each character duration correspons to one mel time step, which translates to hop_length / sample_rate seconds in the wav file.

Tensorboard

You can monitor the training with

  tensorboard dfa_checkpoints

Using Your Own Dataset

Just bring your dataset to the LJSpeech format. We recommend to clean and preprocess the text in the metafile.csv before running the DFA, e.g. lower-case, phonemization etc.

Using Preprocessed Mel Spectrograms

You can provide your own mel spectrogams by setting in the config.yaml:

  precomputed_mels: /path/to/mels

Make sure that the mel names match the ids in the metafile, e.g.

  00001.mel ---> 00001|First sample text