HowTo

LICENSE

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README.md

@@ -1,199 +1,226 @@
 ---
-library_name: transformers
-tags: []
+license: cc-by-nc-sa-4.0
+pipeline_tag: audio-classification
+tags:
+- wav2small
+- valence
+- arousal
+- dominance
+- speech
+- speech-emotion-recognition
 ---
 
-# Model Card for Model ID
+# Wav2Small2.0 - Arousal / Dominance / Valence
 
-<!-- Provide a quick summary of what the model is/does. -->
+Please note that this model is for research purpose only. A commercial [license](https://www.audeering.com/products/devaice/) can be acquired with audEERING. The model expects a raw audio signal 16KHz as input, and outputs: arousal, dominance valence in range [0, 1], as well as Anger/Happiness/Neutral/Sad probability. The model is created following the [Wav2Small paper](https://arxiv.org/abs/2408.13920) and has a total of 17K params.
 
 
+# How To
 
-## Model Details
+```python
+import torch
+import numpy as np
+import torch.nn.functional as F
+import librosa
+from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2PreTrainedModel
+from torch import nn
+from transformers import PretrainedConfig
 
-### 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.
+signal = torch.from_numpy(
+    librosa.load('test.wav', sr=16000)[0])[None, :]
+device = 'cpu'
 
-- **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. -->
+def _prenorm(x, attention_mask=None):
+    '''wav2vec2'''
+    if attention_mask is not None:
+        N = attention_mask.sum(1, keepdim=True)  # here attn msk is unprocessed just the original input
+        x -= x.sum(1, keepdim=True) / N
+        var = (x * x).sum(1, keepdim=True) / N
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+    else:
+        x -= x.mean(1, keepdim=True)  # mean is an onnx operator reducemean saves some ops compared to casting integer N to float and the div
+        var = (x * x).mean(1, keepdim=True)
+    return x / torch.sqrt(var + 1e-7)
 
-## 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. -->
+class Spectrogram(nn.Module):
+    def __init__(self,
+        n_fft=64,   # num cols of DFT
+        n_time=64,  # num rows of DFT matrix
+        hop_length=32,
+        freeze_parameters=True):
 
-[More Information Needed]
 
-### Downstream Use [optional]
+        super().__init__()
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+        fft_window = librosa.filters.get_window('hann', n_time, fftbins=True)
 
-[More Information Needed]
+        fft_window = librosa.util.pad_center(fft_window, size=n_time)
 
-### Out-of-Scope Use
+        
+        
+        
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+        out_channels = n_fft // 2 + 1
+        
+        (x, y) = np.meshgrid(np.arange(n_time), np.arange(n_fft))
+        omega = np.exp(-2 * np.pi * 1j / n_time)
+        dft_matrix = np.power(omega, x * y)  # (n_fft, n_time)
+        dft_matrix = dft_matrix * fft_window[None, :]
+        dft_matrix = dft_matrix[0 : out_channels, :]
+        dft_matrix = dft_matrix[:, None, :]
+        
+        # ---- Assymetric DFT Non Square
 
-[More Information Needed]
+        self.conv_real = nn.Conv1d(1, out_channels, n_fft, stride=hop_length,
+                                   padding=0, bias=False)
+        self.conv_imag = nn.Conv1d(1, out_channels, n_fft, stride=hop_length,
+                                   padding=0, bias=False)
+        self.conv_real.weight.data = torch.tensor(np.real(dft_matrix),
+                                                    dtype=self.conv_real.weight.dtype,
+                                                    device=self.conv_real.weight.device)
+        self.conv_imag.weight.data = torch.tensor(np.imag(dft_matrix),
+                                                    dtype=self.conv_imag.weight.dtype,
+                                                    device=self.conv_imag.weight.device)
+        if freeze_parameters:
+            for param in self.parameters():
+                param.requires_grad = False
 
-## Bias, Risks, and Limitations
+    def forward(self, input):
+        x = input[:, None, :]
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+        real = self.conv_real(x)
+        imag = self.conv_imag(x)
+        return real ** 2 + imag ** 2  # bs, mel, time-frames
 
-[More Information Needed]
 
-### Recommendations
+class LogmelFilterBank(nn.Module):
+    def __init__(self,
+                sr=16000,
+                n_fft=64,
+                n_mels=26,  # maxpool
+                fmin=0.0,
+                freeze_parameters=True):
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+        super().__init__()
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+        fmax = sr//2
 
-## How to Get Started with the Model
+        W2 = librosa.filters.mel(sr=sr,
+                                        n_fft=n_fft,
+                                        n_mels=n_mels,
+                                        fmin=fmin,
+                                        fmax=fmax).T
 
-Use the code below to get started with the model.
+        self.register_buffer('melW', torch.Tensor(W2))
+        self.register_buffer('amin', torch.Tensor([1e-10]))
 
-[More Information Needed]
+    def forward(self, x):
 
-## Training Details
+        x = torch.matmul(x[:, None, :, :].transpose(2, 3), self.melW)   # changes melf not num frames
 
-### Training Data
+        x = torch.where(x > self.amin, x, self.amin)  # not in place
 
-<!-- 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. -->
+        x = 10 * torch.log10(x)
+        return x
 
-[More Information Needed]
 
-### Training Procedure
+class Conv(nn.Module):
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+    def __init__(self, c_in, c_out, k=3, stride=1, padding=1):
 
-#### Preprocessing [optional]
+        super().__init__()
 
-[More Information Needed]
+        self.conv = nn.Conv2d(c_in, c_out, k, stride=stride, padding=padding, bias=False)
+        self.norm = nn.BatchNorm2d(c_out)
 
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        return torch.relu_(x)
 
-#### 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]
+class Vgg7(nn.Module):
 
-## Evaluation
+    def __init__(self):
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+        super().__init__()
 
-### Testing Data, Factors & Metrics
+        self.l1 = Conv( 1, 13)
+        self.l2 = Conv(13, 13)
+        self.l3 = Conv(13, 13)
+        self.maxpool_A = nn.MaxPool2d(3,
+                                    stride=2,
+                                    padding=1)
+        self.l4 = Conv(13, 13)
+        self.l5 = Conv(13, 13)
+        self.l6 = Conv(13, 13)
+        self.l7 = Conv(13, 13)
+        self.lin = nn.Conv2d(13, 13, 1, padding=0, stride=1)
+        self.sof = nn.Conv2d(13, 13, 1, padding=0, stride=1)
+        self.spectrogram_extractor = Spectrogram()
+        self.logmel_extractor = LogmelFilterBank()
 
-#### Testing Data
+    def forward(self, x, attention_mask=None):
+        x = _prenorm(x, attention_mask=attention_mask)
+        x = self.spectrogram_extractor(x)
+        x = self.logmel_extractor(x)
+        x = self.l1(x)
+        x = self.l2(x)
+        x = self.l3(x)
+        x = self.maxpool_A(x)  # reshape here? so these conv will have large kernel
+        x = self.l4(x)
+        x = self.l5(x)
+        x = self.l6(x)
+        x = self.l7(x)
+        x = self.lin(x) * self.sof(x).softmax(2)   # [bs, ch, time-frams, mel]
+        x = x.sum(2)
+        x = torch.cat([x,
+                        torch.bmm(x, x.transpose(1,2))], 2)  # cosine over mel dims
+        return x.reshape(-1, 338)
 
-<!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
+class Wav2SmallConfig(PretrainedConfig):
+    model_type = "wav2vec2"
 
-#### Factors
+    def __init__(self,
+                **kwargs):
+        super().__init__(**kwargs)
+        self.half_mel = 13
+        self.n_fft = 64
+        self.n_time = 64
+        self.hidden = 2 * self.half_mel * self.half_mel
+        self.hop = self.n_time // 2
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
+class Wav2Small(Wav2Vec2PreTrainedModel):
 
-#### Metrics
+    def __init__(self,
+                config):
+        super().__init__(config)
+        self.vgg7 = Vgg7()
+        self.adv  = nn.Linear(config.hidden, 3)   # 0=arousal, 1=dominance, 2=valence
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+    def forward(self, x, attention_mask=None):
+        x = self.vgg7(x, attention_mask=attention_mask)
+        return self.adv(x)
 
-[More Information Needed]
 
-### Results
+model = Wav2Small.from_pretrained(
+    'audeering/wav2small').to(device).eval()
+with torch.no_grad():
+    logits = model(signal.to(device))
 
-[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]
+print(f'\nArousal={logits[:, 0]}\n',
+      f'Dominance={logits[:, 1]}\n',
+      f'Valence={logits[:, 2]}\n')
+```