Upload custom_interface.py

custom_interface.py

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+import torch
+from speechbrain.inference.interfaces import Pretrained
+
+
+class ASR(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, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    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
+        """
+        batch = batch.to(self.device)
+        sig, self.sig_lens = batch.sig
+        tokens_bos, _ = batch.tokens_bos
+        sig, self.sig_lens = sig.to(self.device), self.sig_lens.to(self.device)
+
+        # Forward pass
+        encoded_outputs = self.modules.encoder_w2v2(sig.detach())
+        embedded_tokens = self.modules.embedding(tokens_bos)
+        decoder_outputs, _ = self.modules.decoder(embedded_tokens, encoded_outputs, self.sig_lens)
+
+        # Output layer for seq2seq log-probabilities
+        logits = self.modules.seq_lin(decoder_outputs)
+        predictions = {"seq_logprobs": self.hparams.log_softmax(logits)}
+        predictions["tokens"], _, _, _ = self.hparams.test_search(encoded_outputs, self.sig_lens)
+
+        return predictions
+
+
+    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)["tokens"]
+       
+        return outputs
+
+    def forward(self, wavs, wav_lens=None):
+        return self.encode_batch(wavs=wavs, wav_lens=wav_lens)