WhisperCTC

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Model Details

Model Description

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class WhisperCTC(nn.Module):
    def __init__(
        self,
        encoder_id: str = "tuanio/whisper-encoder.tiny.en",
        dropout: float = 0.1,
        vocab_size: int = 47,
    ):
        super().__init__()
        self.encoder = WhisperEncoder.from_pretrained(encoder_id)
        print("Freezing Whisper Encoder...")
        self.encoder._freeze_parameters()
        print("Freezed!")
        self.lm_head = nn.Sequential(
            nn.SiLU(),
            nn.Dropout(dropout),
            nn.Linear(self.encoder.config.d_model, vocab_size),
        )
        nn.init.kaiming_uniform_(
            self.lm_head[-1].weight, mode="fan_in", nonlinearity="relu"
        )

    def forward(self, feat: Tensor, attn_mask: Tensor):
        enc = self.encoder(
            input_features=feat, attention_mask=attn_mask
        ).last_hidden_state
        logits = self.lm_head(enc)
        log_probs = nn.functional.log_softmax(logits, dim=-1)
        return log_probs

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Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

• IndictTTS: https://www.kaggle.com/datasets/tuannguyenvananh/indictts-english

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

data_cfg:
  dataset:
    processor:
      feat_extractor_id: ${model_cfg.model.encoder_id}
      tokenizer_id: ${model_cfg.tokenizer_id}
    path:
      base:
        indict_tts: ../IndicTTS
        cv: ../
      train:
        - train_data/indict_tts_train.jsonl
        # - train_data/cv_train.jsonl
      test:
        - train_data/indict_tts_test.jsonl
        # - train_data/cv_test.jsonl
      dev:
        - train_data/indict_tts_dev.jsonl
        # - train_data/cv_dev.jsonl
  dataloader:
    batch_size: 46
    num_workers: 8
    pin_memory: True

model_cfg:
  tokenizer_id: tuanio/wav2vec2-phoneme-ipa-ctc
  model:
    dropout: 0.1
    encoder_id: tuanio/whisper-encoder.medium.en
  optim:
    lr: 1.25e-05
    betas: [0.9, 0.998]
    weight_decay: 0.01
  scheduler:
    name: linear
    total_steps: -1
    warmup_ratio: 0.05
    interval: step
    frequency: 1

trainer_cfg:
  log:
    wandb: True
  logger_wandb:
    project: aped_indian-lish
    name: whisper-medium-indict-tts-only-from-epoch1
    log_model: all
  arguments:
    accelerator: gpu
    devices: -1
    max_epochs: 10
    log_every_n_steps: 1
    enable_checkpointing: True
    accumulate_grad_batches: 2
    inference_mode: True
    gradient_clip_val: 5.0
    check_val_every_n_epoch: 1
    val_check_interval: null


experiment_cfg:
  train: True
  valid: True
  test: True
  ckpt:
    resume_ckpt: True
    ckpt_path: ckpt/medium.epoch3.ckpt

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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