Create handler.py

handler.py

@@ -0,0 +1,116 @@
+import torch
+import speechbrain as sb
+
+show_results_every = 100  # plots results every N iterations
+run_opts = {
+    "device": "cuda" if torch.cuda.is_available() else "cpu"
+}
+
+class PipelineSLUTask(sb.pretrained.interfaces.Pretrained):
+    HPARAMS_NEEDED = [
+            "slu_enc",
+            "output_emb",
+            "dec",
+            "seq_lin",
+            "env_corrupt",
+    ]
+    MODULES_NEEDED = [
+            "slu_enc",
+            "output_emb",
+            "dec",
+            "seq_lin",
+            "env_corrupt",
+    ]
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        pass
+
+    def encode_file(self, path):
+
+        tokens_bos = torch.tensor([[0]]).to(self.device)
+        tokens = torch.tensor([], dtype=torch.int64).to(self.device)
+
+        waveform = self.load_audio(path)
+        wavs = waveform.unsqueeze(0)
+        wav_lens = torch.tensor([1.0])
+        # Fake a batch:
+        # batch = waveform.unsqueeze(0)
+        rel_length = torch.tensor([1.0])
+        with torch.no_grad():
+            rel_lens = rel_length.to(self.device)
+            # ASR encoder forward pass
+            ASR_encoder_out = self.hparams.asr_model.encode_batch(
+                wavs.detach(), wav_lens
+            )
+
+            # SLU forward pass
+            encoder_out = self.hparams.slu_enc(ASR_encoder_out)
+            e_in = self.hparams.output_emb(tokens_bos)
+            # print(e_in.shape)
+            # print(encoder_out.shape)
+            # print(wav_lens.shape)
+            h, _ = self.hparams.dec(e_in, encoder_out, wav_lens)
+
+            # Output layer for seq2seq log-probabilities
+            logits = self.hparams.seq_lin(h)
+            p_seq = self.hparams.log_softmax(logits)
+
+            # Compute outputs
+            # if (
+            #     stage == sb.Stage.TRAIN
+            #     and self.batch_count % show_results_every != 0
+            # ):
+            #     return p_seq, wav_lens
+            # else:
+            p_tokens, scores = self.hparams.beam_searcher(encoder_out, wav_lens)
+            return p_seq, wav_lens, p_tokens
+
+        # return ASR_encoder_out
+
+    def decode(self, p_seq, wav_lens, predicted_tokens):
+        tokens_eos = torch.tensor([[0]]).to(self.device)
+        tokens_eos_lens = torch.tensor([0]).to(self.device)
+
+        # Decode token terms to words
+        predicted_semantics = [
+            tokenizer.decode_ids(utt_seq).split(" ")
+            for utt_seq in predicted_tokens
+        ]
+        return predicted_semantics
+
+
+from typing import Dict, List, Any
+from pretrained import PipelineSLUTask
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        hparams_file = f"{path}/direct-train.yaml"
+        overrides = {}
+        with open(hparams_file) as fin:
+            hparams = load_hyperpyyaml(fin, overrides)
+            
+        run_opts = {
+            "device": "cuda" if torch.cuda.is_available() else "cpu"
+        }
+        
+        self.pipeline = PipelineSLUTask(
+            modules=hparams['modules'],
+            hparams=hparams,
+            run_opts=run_opts
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        # pseudo
+        # self.model(input)
+        data = data.get("inputs", data)
+        print(data)
+        ps, wl, pt = self.pipeline.encode_file(data)
+        return self.pipeline.decode(ps, wl, pt)