CustomeEncoderWav2vec2classifier class

custom_interface.py

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+import torch
+from speechbrain.inference.interfaces import Pretrained
+import openvino as ov
+
+class CustomEncoderWav2vec2Classifier(Pretrained):
+    """A ready-to-use class for utterance-level classification (e.g, speaker-id,
+    language-id, emotion recognition, keyword spotting, etc).
+
+    The class assumes that an self-supervised encoder like wav2vec2/hubert and a classifier model
+    are defined in the yaml file. If you want to
+    convert the predicted index into a corresponding text label, please
+    provide the path of the label_encoder in a variable called 'lab_encoder_file'
+    within the yaml.
+
+    The class can be used either to run only the encoder (encode_batch()) to
+    extract embeddings or to run a classification step (classify_batch()).
+    ```
+
+    Example
+    -------
+    >>> import torchaudio
+    >>> from speechbrain.pretrained import EncoderClassifier
+    >>> # Model is downloaded from the speechbrain HuggingFace repo
+    >>> tmpdir = getfixture("tmpdir")
+    >>> classifier = EncoderClassifier.from_hparams(
+    ...     source="speechbrain/spkrec-ecapa-voxceleb",
+    ...     savedir=tmpdir,
+    ... )
+
+    >>> # Compute embeddings
+    >>> signal, fs = torchaudio.load("samples/audio_samples/example1.wav")
+    >>> embeddings =  classifier.encode_batch(signal)
+
+    >>> # Classification
+    >>> prediction =  classifier .classify_batch(signal)
+    """
+
+    def __init__(self, *args, model=None,
+                 audio_file_path=None, backend="pytorch",
+                 ov_opts={"device_name": "cpu"},
+                 save_ov_model=False,
+                 **kwargs):
+        super().__init__(*args, **kwargs)
+        self.backend = backend
+        if self.backend == "openvino":
+            print("=" * 30)
+            print("OpenVINO Backend Selected")
+            print("=" * 30)
+
+            self.core = ov.Core()
+            self.ov_model = None
+            # if torch model
+            if model:
+                print("\n[INFO] Preparing OpenVINO model...")
+                self.get_ov_model(model, audio_file_path)
+                print("[SUCCESS] OpenVINO IR model compiled for inference!\n")
+            if self.ov_model:
+                self.device = ov_opts["device_name"]
+                print("[INFO] Compiling OpenVINO IR model for inference...")
+                self.compiled_model = self.core.compile_model(self.ov_model, config=ov_opts)
+                print("[SUCCESS] OpenVINO IR model compiled for inference!\n")
+                    # Falg to save openvino ir model file to disk
+            if save_ov_model:
+                # set to default path
+                print("[INFO] Saving OpenVINO IR model to disk!\n")
+                ov_ir_file_path = "./openvino_model/fp32/speechbrain_emotion_recog_ov_ir_model.xml"
+                ov.save_model(self.ov_model, ov_ir_file_path)
+                print(f"[SUCCESS] OpenVINO IR model file saved at {ov_ir_file_path}!\n")
+
+    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()
+
+        if self.backend == "pytorch":
+            # Computing features and embeddings
+            outputs = self.mods.wav2vec2(wavs)
+        elif self.backend == "openvino":
+            # OpenVINO inference
+            outputs = self.ov_inference(wavs, wav_lens)
+
+        # last dim will be used for AdaptativeAVG pool
+        outputs = self.mods.avg_pool(outputs, wav_lens)
+        outputs = outputs.view(outputs.shape[0], -1)
+
+        return outputs
+
+    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).
+        """
+        outputs = self.encode_batch(wavs, wav_lens)
+        outputs = self.mods.output_mlp(outputs)
+        out_prob = self.hparams.softmax(outputs)
+        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])
+        outputs = self.encode_batch(batch, rel_length)
+        outputs = self.mods.output_mlp(outputs).squeeze(1)
+        out_prob = self.hparams.softmax(outputs)
+        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 get_ov_model(self, torch_model, path):
+        # Prepare input tensor
+        waveform = self.load_audio(path)
+        wavs = waveform.unsqueeze(0)
+
+        # Torch to OpenVINO model conversion
+        self.ov_model = ov.convert_model(torch_model, example_input=wavs)
+
+    def ov_inference(self, wavs, wav_lens):
+        output_tensor = self.compiled_model(wavs.float())[0]
+        output_tensor = torch.from_numpy(output_tensor)
+        print("\n[INFO] Performing OpenVINO inference...")
+
+        return output_tensor
+
+    def forward(self, wavs, wav_lens=None, normalize=False):
+        return self.encode_batch(
+            wavs=wavs, wav_lens=wav_lens, normalize=normalize
+        )