Create custom_interface.py

custom_interface.py

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+import torch
+from speechbrain.inference.interfaces import Pretrained
+import librosa
+
+
+class ASR(Pretrained):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def encode_batch(self, device, wavs, wav_lens=None, normalize=False):
+        wavs = wavs.to(device)
+        wav_lens = wav_lens.to(device)
+
+        # Forward pass
+        encoded_outputs = self.mods.encoder_w2v2(wavs.detach())
+        # append
+        tokens_bos = torch.zeros((wavs.size(0), 1), dtype=torch.long).to(device)
+        embedded_tokens = self.mods.embedding(tokens_bos)
+        decoder_outputs, _ = self.mods.decoder(embedded_tokens, encoded_outputs, wav_lens)
+
+        # Output layer for seq2seq log-probabilities
+        predictions = self.hparams.test_search(encoded_outputs, wav_lens)[0]
+        # predicted_words = [self.hparams.tokenizer.decode_ids(prediction).split(" ") for prediction in predictions]
+        predicted_words = []
+        for prediction in predictions:
+            prediction = [token for token in prediction if token != 0]
+            predicted_words.append(self.hparams.tokenizer.decode_ids(prediction).split(" "))
+        prediction = []
+        for sent in predicted_words:
+            sent = self.filter_repetitions(sent, 3)
+            prediction.append(sent)
+        predicted_words = prediction
+        return predicted_words
+
+    def filter_repetitions(self, seq, max_repetition_length):
+        seq = list(seq)
+        output = []
+        max_n = len(seq) // 2
+        for n in range(max_n, 0, -1):
+            max_repetitions = max(max_repetition_length // n, 1)
+            # Don't need to iterate over impossible n values:
+            # len(seq) can change a lot during iteration
+            if (len(seq) <= n*2) or (len(seq) <= max_repetition_length):
+                continue
+            iterator = enumerate(seq)
+            # Fill first buffers:
+            buffers = [[next(iterator)[1]] for _ in range(n)]
+            for seq_index, token in iterator:
+                current_buffer = seq_index % n
+                if token != buffers[current_buffer][-1]:
+                    # No repeat, we can flush some tokens
+                    buf_len = sum(map(len, buffers))
+                    flush_start = (current_buffer-buf_len) % n
+                    # Keep n-1 tokens, but possibly mark some for removal
+                    for flush_index in range(buf_len - buf_len%n):
+                        if (buf_len - flush_index) > n-1:
+                            to_flush = buffers[(flush_index + flush_start) % n].pop(0)
+                        else:
+                            to_flush = None
+                        # Here, repetitions get removed:
+                        if (flush_index // n < max_repetitions) and to_flush is not None:
+                            output.append(to_flush)
+                        elif (flush_index // n >= max_repetitions) and to_flush is None:
+                            output.append(to_flush)
+                buffers[current_buffer].append(token)
+            # At the end, final flush
+            current_buffer += 1
+            buf_len = sum(map(len, buffers))
+            flush_start = (current_buffer-buf_len) % n
+            for flush_index in range(buf_len):
+                to_flush = buffers[(flush_index + flush_start) % n].pop(0)
+                # Here, repetitions just get removed:
+                if flush_index // n < max_repetitions:
+                    output.append(to_flush)
+            seq = []
+            to_delete = 0
+            for token in output:
+                if token is None:
+                    to_delete += 1
+                elif to_delete > 0:
+                    to_delete -= 1
+                else:
+                    seq.append(token)
+            output = []
+        return seq
+
+
+    def classify_file(self, path, device):
+        waveform, sr = librosa.load(path, sr=16000)
+
+        waveform = torch.tensor(waveform).to(device)
+        waveform = waveform.to(device)
+        # Fake a batch:
+        batch = waveform.unsqueeze(0)
+        rel_length = torch.tensor([1.0]).to(device)
+        outputs = self.encode_batch(device, batch, rel_length)
+        outputs = " ".join(outputs[0])
+        return outputs