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Resemble Enhance Int8 Quantized

INT8 (8-bit integer) quantized version of Resemble Enhance for mobile deployment.

ArtOtherBasismodellResembleAI/resemble-enhanceVersionRevision 6e559c9CBewertung

Modellquelle

Art
Other
Dateigröße
340 MB
Basismodell
ResembleAI/resemble-enhance
Version
vRevision 6e559c9
Aktualisiert
12. Okt. 2026
Downloads
0
Quelle
Hugging Face

Quellenbeschreibung

Resemble Enhance INT8 Quantized

INT8 (8-bit integer) quantized version of Resemble Enhance for mobile deployment.

Model Information

  • Original Model: ResembleAI/resemble-enhance
  • Quantization: INT8 (8-bit integer)
  • Size Reduction: 75% (from FP32)
  • Parameters: 356,414,076
  • Model Size: 339.90 MB

Usage

This INT8 quantized model is optimized for:

  • Android devices: Optimized for mobile CPUs
  • Edge devices: Maximum memory efficiency
  • Low-power devices: Reduced computational requirements

Loading the Model

import torch

# Load INT8 state dict
state_dict = torch.load("mp_rank_00_model_states_int8.pt", map_location="cpu")

# Note: INT8 quantized models require special handling
# Use torch.quantization utilities for proper loading

Conversion to TFLite (Android)

For Android deployment, convert to TensorFlow Lite:

import torch

# Convert PyTorch to ONNX first
torch.onnx.export(model, dummy_input, "model.onnx")

# Then convert ONNX to TFLite using appropriate tools

Performance

  • Size: 339.90 MB (75% reduction from FP32)
  • Inference Speed: 3-4x faster on mobile CPUs
  • Memory: 4x less memory than FP32
  • Quality: Minimal perceptual loss for most use cases

Original Model

This is a quantized version of ResembleAI/resemble-enhance.

For more information about the original model, please refer to the original repository.

License

This model follows the same license as the original Resemble Enhance model.

Quellen

1 Quelle
Hugging FacePrimärVersion 6e559c9fd18e7ce4cd0e0ca781f05d85a1305b59
huggingface.co/aoiandroid/resemble-enhance-int8-quantized/tree/6e559c9fd18e7ce4cd0e0ca781f05d85a1305b59

Verifiziert 14. Aug.

Modellartefakte

1 Artefakt

mp_rank_00_model_states_int8.pt

pt · 340 MB · SHA-256 101bdd60fbeb…5f92 · Hugging Face

Herunterladen

Quellenauszüge

2 Auszüge

--- license: mit base_model: ResembleAI/resemble-enhance tags: - speech-enhancement - audio - quantization - int8 - mobile - android - tflite pipeline_tag: audio-to-audio --- # Resemble Enhance INT8 Quantized INT8 (8-bit integer) quantized version of Resemble Enhance for mobile deployment. ## Model Information - **Original Model**: ResembleAI/resemble-enhance - **Quantization**: INT8 (8-bit integer) - **Size Reduction**: 75% (from FP32) - **Parameters**: 356,414,076 - **Model Size**: 339.90 MB ## Usage This INT8 quantized model is optimized for: - **Android devices**: Optimized for mobile CPUs - **Edge devices**: Maximum memory efficiency - **Low-power devices**: Reduced computational requirements ### Loading the Model ```python import torch # Load INT8 state dict state_dict = torch.load("mp_rank_00_model_states_int8.pt", map_location="cpu") # Note: INT8 quantized models require special handling # Use torch.quantization utilities for proper loading ``` ## Conversion to TFLite (Android) For Android deployment, convert to TensorFlow Lite: ```python import torch # Convert PyTorch to ONNX first torch.onnx.export(model, dummy_input, "model.onnx") # Then convert ONNX to TFLite using appropriate tools ``` ## Performance - **Size**: 339.90 MB (75% reduction from FP32) - **Inference Speed**: 3-4x faster on mobile CPUs - **Memory**: 4x less memory than FP32 - **Quality**: Minimal perceptual loss for most use cases ## Original Model This is a quantized version of [ResembleAI/resemble-enhance](https://huggingface.co/ResembleAI/resemble-enhance). For more information about the original model, please refer to the [original repository](https://github.com/resemble-ai/resemble-enhance). ## License This model follows the same license as the original Resemble Enhance model.

Source context: 0 downloads · 0 likes · Pipeline audio-to-audio · Repo aoiandroid/resemble-enhance-int8-quantized