license: apache-2.0
datasets:
- openslr/librispeech_asr
language:
- en
metrics:
- wer
tags:
- transformers
- pytorch
- speech-to-text
- conformer
- embedded
- edgeAI
- ExecuTorch
- audioprocessing
- transformer
Model Card for Model ID
Conformer is a popular Transformer based speech recognition network, suitable for embedded devices. This repository contains FP32 trained weights and the associated tokenizer for an implementation of Conformer. We also include exported quantized program with ExecuTorch, quantized for the ExecuTorch Ethos-U backend allowing an easy deployment on SoCs with an Arm® Ethos™-U NPU.
Model Details
Model Description
Conformer is a popular Neural Network for speech recognition. This repository contains trained weights for the Conformer implementation in https://github.com/sooftware/conformer/
- Developed by: Arm
- Model type: Transformer
- Language(s) (NLP): English
- License: Apache-2
Model Sources [optional]
- Repository: https://github.com/sooftware/conformer/
- Paper [optional]: https://arxiv.org/abs/2005.08100
Uses
You need to install ExecuTorch 1.0 with $ pip install executorch.
By downloading the quantized exported graph module, you can directly call the to_edge_transform_and_lower API of ExecuTorch.
The to_edge_transform_and_lower API will convert the quantized exported program to backend-specific command stream for the Ethos-U.
The end result a pte file for your variant of the Ethos-U.
Below is an example script to produce a pte file for Ethos-U85 256 MAC configuration in Shared_Sram memory mode.
import torch
from executorch.backends.arm.ethosu import EthosUPartitioner, EthosUCompileSpec
from executorch.backends.arm.quantizer import (
EthosUQuantizer,
get_symmetric_quantization_config,
)
from executorch.exir import (
EdgeCompileConfig,
ExecutorchBackendConfig,
to_edge_transform_and_lower,
)
from executorch.extension.export_util.utils import save_pte_program
def main():
quant_exported_program = torch.export.load("Conformer_ArmQuantizer_quant_exported_program.pt2")
compile_spec = EthosUCompileSpec(
target="ethos-u85-256",
system_config="Ethos_U85_SYS_Flash_High",
memory_mode="Shared_Sram",
extra_flags=["--output-format=raw", "--debug-force-regor"],
)
partitioner = EthosUPartitioner(compile_spec)
print(
"Calling to_edge_transform_and_lower - lowering to TOSA and compiling for the Ethos-U hardware"
)
# Lower the exported program to the Ethos-U backend
edge_program_manager = to_edge_transform_and_lower(
quant_exported_program,
partitioner=[partitioner],
compile_config=EdgeCompileConfig(
_check_ir_validity=False,
),
)
executorch_program_manager = edge_program_manager.to_executorch(
config=ExecutorchBackendConfig(extract_delegate_segments=False)
)
save_pte_program(
executorch_program_manager, f"conformer_quantized.pte"
)
if __name__ == "__main__":
main()
Bias, Risks, and Limitations
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Recommendations
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
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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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