Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.
2026-07-09 22:38:58 +08:00
commit 6116b1f3c6
3683 changed files with 990984 additions and 0 deletions
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+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+import types
+import copy
+import torch
+from funasr.register import tables
+
+
+def export_rebuild_model(model, **kwargs):
+    # self.device = kwargs.get("device")
+    """Export rebuild model.
+    
+        Args:
+            model: Model instance or model name.
+            **kwargs: Additional keyword arguments.
+        """
+    is_onnx = kwargs.get("type", "onnx") == "onnx"
+    encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
+
+    if hasattr(model, "ctc"):
+        model.encoder = encoder_class(
+            model.encoder,
+            onnx=is_onnx,
+            feats_dim=kwargs.get("input_size", 560),
+            ctc_linear=model.ctc.ctc_lo
+        )
+    else:
+        assert False, print(model)
+        model.encoder = encoder_class(model.encoder, onnx=is_onnx, feats_dim=kwargs.get("input_size", 560))
+
+    # from funasr.utils.torch_function import sequence_mask
+    # model.make_pad_mask = sequence_mask(max_seq_len=None, flip=False)
+
+    encoder_model = copy.copy(model)
+
+    # encoder
+    encoder_model.forward = types.MethodType(export_encoder_forward, encoder_model)
+    encoder_model.export_dummy_inputs = types.MethodType(export_encoder_dummy_inputs, encoder_model)
+    encoder_model.export_input_names = types.MethodType(export_encoder_input_names, encoder_model)
+    encoder_model.export_output_names = types.MethodType(export_encoder_output_names, encoder_model)
+    encoder_model.export_dynamic_axes = types.MethodType(export_encoder_dynamic_axes, encoder_model)
+    encoder_model.export_name = types.MethodType(export_encoder_name, encoder_model)
+
+    return encoder_model
+
+
+def export_encoder_forward(
+    self,
+    speech: torch.Tensor,
+    speech_lengths: torch.Tensor,
+):
+    # a. To device
+    """Export encoder forward.
+    
+        Args:
+            speech: Speech audio tensor, shape (batch, time).
+            speech_lengths: Length of each speech sample.
+        """
+    batch = {
+        "speech": speech,
+        "speech_lengths": speech_lengths,
+        "online": True
+    }
+    # batch = to_device(batch, device=self.device)
+
+    encoder_out, encoder_out_len = self.encoder(**batch)
+    # mask = self.make_pad_mask(encoder_out_len)[:, None, :]
+    # alphas, _ = self.predictor.forward_cnn(enc, mask)
+
+    # return encoder_out, encoder_out_len, alphas
+    return encoder_out, encoder_out_len
+
+
+def export_encoder_dummy_inputs(self):
+    """Export encoder dummy inputs."""
+    speech = torch.randn(2, 30, 280)
+    speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
+    return (speech, speech_lengths)
+
+
+def export_encoder_input_names(self):
+    """Export encoder input names."""
+    return ["speech", "speech_lengths"]
+
+
+def export_encoder_output_names(self):
+    # return ["encoder_out", "encoder_out_len", "alphas"]
+    """Export encoder output names."""
+    return ["encoder_out", "encoder_out_len"]
+
+
+def export_encoder_dynamic_axes(self):
+    """Export encoder dynamic axes."""
+    return {
+        "speech": {
+            0: "batch_size", 1: "feats_length"
+        },
+        "speech_lengths": {
+            0: "batch_size",
+        },
+        "encoder_out": {
+            0: "batch_size", 1: "feats_length"
+        },
+        "encoder_out_len": {
+            0: "batch_size",
+        },
+    }
+
+
+def export_encoder_name(self):
+    """Export encoder name."""
+    return "encoder.onnx"