HiDolen
/

Mini-BS-RoFormer

@@ -1,199 +1,118 @@
 ---
 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]

 ---
 library_name: transformers
+license: mit
+datasets:
+- CLAPv2/MUSDB18-HQ
+pipeline_tag: audio-to-audio
+tags:
+- music
 ---
 # Model Card for Model ID
+Model for the Music source separation task. Its implementation is referenced to [the existing BS-RoFormer code](https://github.com/lucidrains/BS-RoFormer).
+针对音乐音频分离任务的模型。改编自 [现有的 BS-RoFormer 模型代码](https://github.com/lucidrains/BS-RoFormer)。
 ## Model Details
+模型参数：
+- depth = 3
+- hidden_size = 256
+- intermediate_size = 256 * 2
+总参数量只有 8.8M，在 MUSDB18HQ 数据的 val 集上达到平均 SDR 6.5 的性能。分轨具体 SDR：
+- bass，5.66
+- drums，6.77
+- other，6.06
+- vocal，7.44
 ## Uses
+使用的 transformers 库版本为 4.55.4。为了正常运行模型还需要安装库 soudfile 和 einops。
+CPU 推理：
+```python
+from transformers import AutoModel
+import soundfile
+import torch
+model_name = "HiDolen/Mini-BS-RoFormer"
+model = AutoModel.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+)
+# 加载音频
+file = "./Bruno Mars - Runaway Baby.mp3"
+waveform, sr = soundfile.read(file)
+assert sr == 44100  # 采样率必须为 44100Hz
+waveform = torch.tensor(waveform).T.float()
+# 进行推理
+result = model.separate(
+    waveform,
+    chunk_size=44100 * 6,
+    overlap_size=44100 * 3,
+    gap_size=0,
+    batch_size=16,
+    verbose=True,
+)
+# 保存处理结果
+for i in range(result.shape[0]):
+    soundfile.write(f"separated_stem_{i}.wav", result[i].cpu().numpy().T, 44100)
+```
+GPU 推理：
+```python
+from transformers import AutoModel
+import soundfile
+import torch
+model_name = "HiDolen/Mini-BS-RoFormer"
+model = AutoModel.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+)
+model.to("cuda")
+# 加载音频
+file = "./Bruno Mars - Runaway Baby.mp3"
+waveform, sr = soundfile.read(file)
+assert sr == 44100  # 采样率必须为 44100Hz
+waveform = torch.tensor(waveform).T.float()
+waveform.to("cuda")
+# 进行推理
+result = model.separate(
+    waveform,
+    chunk_size=44100 * 6,
+    overlap_size=44100 * 3,
+    gap_size=0,
+    batch_size=16,
+    verbose=True,
+)
+# 保存处理结果
+for i in range(result.shape[0]):
+    soundfile.write(f"separated_stem_{i}.wav", result[i].cpu().numpy().T, 44100)
+```
 ## Training Details
+使用 MUSDB18HQ 数据进行训练。
+不使用原论文中提到的 Multi-STFT 损失项以提高训练速度。
+学习率 5e-4，以 batch_size=6 训练 60k 步。
+## Acknowledgments
+- https://github.com/lucidrains/BS-RoFormer
+- https://arxiv.org/abs/2309.02612 (Music Source Separation with Band-Split RoPE Transformer)