Upload ConformerEncoder

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure 
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+

config.json

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+{
+  "_name_or_path": "tiny/checkpoint-1147800",
+  "architectures": [
+    "ConformerEncoder"
+  ],
+  "auto_map": {
+    "AutoConfig": "conformer.ConformerConfig",
+    "AutoModel": "conformer.ConformerEncoder"
+  },
+  "conformer_depthwise_conv_kernel_size": 31,
+  "conformer_dropout": 0.1,
+  "conformer_ffn_dim": 576,
+  "conformer_input_dim": 144,
+  "conformer_num_heads": 4,
+  "conformer_num_layers": 8,
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": true,
+  "input_dim": 80,
+  "model_type": "conformer",
+  "output_dim": 40,
+  "pad_token_id": 39,
+  "time_reduction_stride": 4,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2"
+}

conformer.py

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+from torchaudio.models import Conformer
+from torchaudio.models.rnnt import _TimeReduction
+from transformers import PretrainedConfig, PreTrainedModel
+import torch
+from torch import nn
+from typing import List, Tuple, Optional
+
+
+class ConformerConfig(PretrainedConfig):
+    model_type = 'conformer'
+
+
+class ConformerEncoder(PreTrainedModel):
+    config_class = ConformerConfig
+
+    def __init__(
+        self,
+        config,
+    ) -> None:
+        super().__init__(config)
+        self.time_reduction = _TimeReduction(config.time_reduction_stride)
+        self.input_linear = torch.nn.Linear(
+            config.input_dim * config.time_reduction_stride,
+            config.conformer_input_dim)
+        self.conformer = Conformer(
+            num_layers=config.conformer_num_layers,
+            input_dim=config.conformer_input_dim,
+            ffn_dim=config.conformer_ffn_dim,
+            num_heads=config.conformer_num_heads,
+            depthwise_conv_kernel_size=config.conformer_depthwise_conv_kernel_size,
+            dropout=config.conformer_dropout,
+            use_group_norm=True,
+            convolution_first=True,
+        )
+        self.output_linear = torch.nn.Linear(config.conformer_input_dim, config.output_dim)
+
+    def forward(self, inputs, lengths, labels=None):
+        time_reduction_out, time_reduction_lengths = self.time_reduction(inputs, lengths)
+        input_linear_out = self.input_linear(time_reduction_out)
+        x, input_lengths = self.conformer(input_linear_out, time_reduction_lengths)
+        logits = self.output_linear(x)
+
+        loss = None
+        if labels is not None:
+            labels_mask = labels >= 0
+            target_lengths = labels_mask.sum(-1)
+            flattened_targets = labels.masked_select(labels_mask)
+            log_probs = nn.functional.log_softmax(
+                logits,
+                dim=-1,
+                dtype=torch.float32
+            ).transpose(0, 1)
+
+            with torch.backends.cudnn.flags(enabled=False):
+                loss = nn.functional.ctc_loss(
+                    log_probs,
+                    flattened_targets,
+                    input_lengths,
+                    target_lengths,
+                    blank=self.config.pad_token_id,
+                    reduction=self.config.ctc_loss_reduction,
+                    zero_infinity=self.config.ctc_zero_infinity,
+                )
+
+        output = (logits, input_lengths)
+        return ((loss,) + output) if loss is not None else output

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c6a6acc4ede9db075d6be3ed86f9bf9992c58f64ceb4a5f600f179c19bbab93e
+size 15780592