Model Card for Model ID

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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

Model Sources [optional]

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Uses

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Direct Use

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Downstream Use [optional]

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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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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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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Factors

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Metrics

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Results

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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).

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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