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 torch
+from funasr.register import tables
+
+
+def export_rebuild_model(model, **kwargs):
+    """Export rebuild model.
+    
+        Args:
+            model: Model instance or model name.
+            **kwargs: Additional keyword arguments.
+        """
+    model.device = kwargs.get("device")
+    is_onnx = kwargs.get("type", "onnx") == "onnx"
+    encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
+    model.encoder = encoder_class(model.encoder, onnx=is_onnx)
+
+    predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
+    model.predictor = predictor_class(model.predictor, onnx=is_onnx)
+
+    decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
+    model.decoder = decoder_class(model.decoder, onnx=is_onnx)
+
+    from funasr.utils.torch_function import sequence_mask
+
+    model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)
+
+    model.forward = types.MethodType(export_forward, model)
+    model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
+    model.export_input_names = types.MethodType(export_input_names, model)
+    model.export_output_names = types.MethodType(export_output_names, model)
+    model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
+    model.export_name = types.MethodType(export_name, model)
+
+    model.export_name = 'model'
+    return model
+
+
+def export_forward(
+    self,
+    speech: torch.Tensor,
+    speech_lengths: torch.Tensor,
+):
+    # a. To device
+    """Export forward.
+    
+        Args:
+            speech: Speech audio tensor, shape (batch, time).
+            speech_lengths: Length of each speech sample.
+        """
+    batch = {"speech": speech, "speech_lengths": speech_lengths}
+    # batch = to_device(batch, device=self.device)
+
+    enc, enc_len = self.encoder(**batch)
+    mask = self.make_pad_mask(enc_len)[:, None, :]
+    pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
+    pre_token_length = pre_token_length.floor().type(torch.int32)
+
+    decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
+    decoder_out = torch.log_softmax(decoder_out, dim=-1)
+    # sample_ids = decoder_out.argmax(dim=-1)
+
+    return decoder_out, pre_token_length
+
+
+def export_dummy_inputs(self):
+    """Export dummy inputs."""
+    speech = torch.randn(2, 30, 560)
+    speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
+    return (speech, speech_lengths)
+
+
+def export_input_names(self):
+    """Export input names."""
+    return ["speech", "speech_lengths"]
+
+
+def export_output_names(self):
+    """Export output names."""
+    return ["logits", "token_num"]
+
+
+def export_dynamic_axes(self):
+    """Export dynamic axes."""
+    return {
+        "speech": {0: "batch_size", 1: "feats_length"},
+        "speech_lengths": {
+            0: "batch_size",
+        },
+        "logits": {0: "batch_size", 1: "logits_length"},
+    }
+
+
+def export_name(
+    self,
+):
+    """Export name."""
+    return "model.onnx"