commit predictor test

README.md

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+---
+language: "en"
+thumbnail:
+tags:
+- speechbrain
+- embeddings
+- Speaker
+- Verification
+- Identification
+- pytorch
+- ECAPA
+- TDNN
+license: "apache-2.0"
+datasets:
+- voxceleb
+metrics:
+- EER
+widget:
+- label: VoxCeleb Speaker id10003
+  src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb1_00003.wav
+- label: VoxCeleb Speaker id10004
+  src: https://cdn-media.huggingface.co/speech_samples/VoxCeleb_00004.wav
+---
+
+<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
+<br/><br/>
+
+# Speaker Verification with ECAPA-TDNN embeddings on Voxceleb
+
+This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. 
+The system can be used to extract speaker embeddings as well. 
+It is trained on Voxceleb 1+ Voxceleb2 training data. 
+
+For a better experience, we encourage you to learn more about
+[SpeechBrain](https://speechbrain.github.io). The model performance on Voxceleb1-test set(Cleaned) is:
+
+| Release | EER(%) | minDCF | 
+|:-------------:|:--------------:|:--------------:|
+| 05-03-21 | 0.69 | 0.08258 | 
+
+
+## Pipeline description
+
+This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss.  Speaker Verification is performed using cosine distance between speaker embeddings.
+
+## Install SpeechBrain
+
+First of all, please install SpeechBrain with the following command:
+
+```
+gh repo clone aheba/speechbrain-aheba-contribs
+git checkout pretrain_new
+pip install -r requirements.txt
+pip install --editable .
+```
+
+Please notice that we encourage you to read our tutorials and learn more about
+[SpeechBrain](https://speechbrain.github.io).
+
+### Compute your speaker embeddings
+
+```python
+import torchaudio
+from speechbrain.pretrained import Predictor
+classifier = Predictor.import_model(source="aheba31/test-predictor")
+signal, fs = torchaudio.load('samples/audio_samples/example1.wav')
+embeddings = classifier.encode_batch(signal)
+```
+
+### Perform Speaker Verification
+
+```python
+from speechbrain.pretrained import SpeakerRecognition
+verification = SpeakerRecognition.from_hparams(source="aheba31/test-predictor", savedir="aheba31/test-predictor")
+score, prediction = verification.verify_files("speechbrain/spkrec-ecapa-voxceleb/example1.wav", "speechbrain/spkrec-ecapa-voxceleb/example2.flac")
+```
+ The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.
+
+### Inference on GPU
+To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
+
+### Training
+The model was trained with SpeechBrain (aa018540).
+To train it from scratch follows these steps:
+1. Clone SpeechBrain:
+```bash
+git clone https://github.com/speechbrain/speechbrain/
+```
+2. Install it:
+```
+cd speechbrain
+pip install -r requirements.txt
+pip install -e .
+```
+
+3. Run Training:
+```
+cd  recipes/VoxCeleb/SpeakerRec
+python train_speaker_embeddings.py hparams/train_ecapa_tdnn.yaml --data_folder=your_data_folder
+```
+
+You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
+
+### Limitations
+The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
+
+#### Referencing ECAPA-TDNN
+```
+@inproceedings{DBLP:conf/interspeech/DesplanquesTD20,
+  author    = {Brecht Desplanques and
+               Jenthe Thienpondt and
+               Kris Demuynck},
+  editor    = {Helen Meng and
+               Bo Xu and
+               Thomas Fang Zheng},
+  title     = {{ECAPA-TDNN:} Emphasized Channel Attention, Propagation and Aggregation
+               in {TDNN} Based Speaker Verification},
+  booktitle = {Interspeech 2020},
+  pages     = {3830--3834},
+  publisher = {{ISCA}},
+  year      = {2020},
+}
+```
+
+# **Citing SpeechBrain**
+Please, cite SpeechBrain if you use it for your research or business.
+
+```bibtex
+@misc{speechbrain,
+  title={{SpeechBrain}: A General-Purpose Speech Toolkit},
+  author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio},
+  year={2021},
+  eprint={2106.04624},
+  archivePrefix={arXiv},
+  primaryClass={eess.AS},
+  note={arXiv:2106.04624}
+}
+```
+
+# **About SpeechBrain**
+- Website: https://speechbrain.github.io/
+- Code: https://github.com/speechbrain/speechbrain/
+- HuggingFace: https://huggingface.co/speechbrain/

classifier.ckpt

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+size 5534328

config.json

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+{
+  "speechbrain_interface": "SpeakerRecognition"
+}

embedding_model.ckpt

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+size 83316686

hyperparams.yaml

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+# ############################################################################
+# Model: ECAPA big for Speaker verification
+# ############################################################################
+
+# Hparams NEEDED
+HPARAMS_NEEDED: ["label_encoder"]
+# Modules Needed
+MODULES_NEEDED: ["compute_features", "mean_var_norm", "embedding_model", "classifier"]
+
+# Feature parameters
+n_mels: 80
+
+# Pretrain folder (HuggingFace)
+pretrained_path: speechbrain/spkrec-ecapa-voxceleb
+
+# Output parameters
+out_n_neurons: 7205
+
+# Model params
+compute_features: !new:speechbrain.lobes.features.Fbank
+    n_mels: !ref <n_mels>
+
+mean_var_norm: !new:speechbrain.processing.features.InputNormalization
+    norm_type: sentence
+    std_norm: False
+
+embedding_model: !new:speechbrain.lobes.models.ECAPA_TDNN.ECAPA_TDNN
+    input_size: !ref <n_mels>
+    channels: [1024, 1024, 1024, 1024, 3072]
+    kernel_sizes: [5, 3, 3, 3, 1]
+    dilations: [1, 2, 3, 4, 1]
+    attention_channels: 128
+    lin_neurons: 192
+
+classifier: !new:speechbrain.lobes.models.ECAPA_TDNN.Classifier
+    input_size: 192
+    out_neurons: !ref <out_n_neurons>
+
+mean_var_norm_emb: !new:speechbrain.processing.features.InputNormalization
+    norm_type: global
+    std_norm: False
+    update_until_epoch: -1  # Freeze the normalization
+
+modules:
+    compute_features: !ref <compute_features>
+    mean_var_norm: !ref <mean_var_norm>
+    embedding_model: !ref <embedding_model>
+    mean_var_norm_emb: !ref <mean_var_norm_emb>
+    classifier: !ref <classifier>
+        
+label_encoder: !new:speechbrain.dataio.encoder.CategoricalEncoder
+
+        
+pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
+    loadables:
+        embedding_model: !ref <embedding_model>
+        mean_var_norm_emb: !ref <mean_var_norm_emb>
+        classifier: !ref <classifier>
+        label_encoder: !ref <label_encoder>
+    paths:
+        embedding_model: !ref <pretrained_path>/embedding_model.ckpt
+        mean_var_norm_emb: !ref <pretrained_path>/mean_var_norm_emb.ckpt
+        classifier: !ref <pretrained_path>/classifier.ckpt
+        label_encoder: !ref <pretrained_path>/label_encoder.txt
+

inference.py

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+
+def forward(self, wavs, wav_lens=None):
+    """Runs the classification"""
+    return self.classify_batch(wavs, wav_lens)
+
+def encode_batch(self, wavs, wav_lens=None, normalize=False):
+    """Encodes the input audio into a single vector embedding.
+
+    The waveforms should already be in the model's desired format.
+    You can call:
+    ``normalized = <this>.normalizer(signal, sample_rate)``
+    to get a correctly converted signal in most cases.
+
+    Arguments
+    ---------
+    wavs : torch.tensor
+        Batch of waveforms [batch, time, channels] or [batch, time]
+        depending on the model. Make sure the sample rate is fs=16000 Hz.
+    wav_lens : torch.tensor
+        Lengths of the waveforms relative to the longest one in the
+        batch, tensor of shape [batch]. The longest one should have
+        relative length 1.0 and others len(waveform) / max_length.
+        Used for ignoring padding.
+    normalize : bool
+        If True, it normalizes the embeddings with the statistics
+        contained in mean_var_norm_emb.
+
+    Returns
+    -------
+    torch.tensor
+        The encoded batch
+    """
+    # Manage single waveforms in input
+    if len(wavs.shape) == 1:
+        wavs = wavs.unsqueeze(0)
+
+    # Assign full length if wav_lens is not assigned
+    if wav_lens is None:
+        wav_lens = torch.ones(wavs.shape[0], device=self.device)
+
+    # Storing waveform in the specified device
+    wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
+    wavs = wavs.float()
+
+    # Computing features and embeddings
+    feats = self.mods.compute_features(wavs)
+    feats = self.mods.mean_var_norm(feats, wav_lens)
+    embeddings = self.mods.embedding_model(feats, wav_lens)
+    if normalize:
+        embeddings = self.hparams.mean_var_norm_emb(
+            embeddings, torch.ones(embeddings.shape[0], device=self.device)
+        )
+    return embeddings
+
+def classify_batch(self, wavs, wav_lens=None):
+    """Performs classification on the top of the encoded features.
+
+    It returns the posterior probabilities, the index and, if the label
+    encoder is specified it also the text label.
+
+    Arguments
+    ---------
+    wavs : torch.tensor
+        Batch of waveforms [batch, time, channels] or [batch, time]
+        depending on the model. Make sure the sample rate is fs=16000 Hz.
+    wav_lens : torch.tensor
+        Lengths of the waveforms relative to the longest one in the
+        batch, tensor of shape [batch]. The longest one should have
+        relative length 1.0 and others len(waveform) / max_length.
+        Used for ignoring padding.
+
+    Returns
+    -------
+    out_prob
+        The log posterior probabilities of each class ([batch, N_class])
+    score:
+        It is the value of the log-posterior for the best class ([batch,])
+    index
+        The indexes of the best class ([batch,])
+    text_lab:
+        List with the text labels corresponding to the indexes.
+        (label encoder should be provided).
+    """
+    emb = self.encode_batch(wavs, wav_lens)
+    out_prob = self.mods.classifier(emb).squeeze(1)
+    score, index = torch.max(out_prob, dim=-1)
+    text_lab = self.hparams.label_encoder.decode_torch(index)
+    return out_prob, score, index, text_lab
+
+
+def classify_file(self, path):
+    """Classifies the given audiofile into the given set of labels.
+
+    Arguments
+    ---------
+    path : str
+        Path to audio file to classify.
+
+    Returns
+    -------
+    out_prob
+        The log posterior probabilities of each class ([batch, N_class])
+    score:
+        It is the value of the log-posterior for the best class ([batch,])
+    index
+        The indexes of the best class ([batch,])
+    text_lab:
+        List with the text labels corresponding to the indexes.
+        (label encoder should be provided).
+    """
+    waveform = self.load_audio(path)
+    # Fake a batch:
+    batch = waveform.unsqueeze(0)
+    rel_length = torch.tensor([1.0])
+    emb = self.encode_batch(batch, rel_length)
+    out_prob = self.mods.classifier(emb).squeeze(1)
+    score, index = torch.max(out_prob, dim=-1)
+    text_lab = self.hparams.label_encoder.decode_torch(index)
+    return out_prob, score, index, text_lab