Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

funasr/models/sanm_kws_streaming/export_meta.py

@@ -0,0 +1,115 @@
+#!/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"

funasr/models/sanm_kws_streaming/model.py

@@ -0,0 +1,499 @@
+#!/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 time
+import torch
+import logging
+from typing import Dict, Tuple
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+
+from funasr.register import tables
+from funasr.models.ctc.ctc import CTC
+from funasr.utils import postprocess_utils
+from funasr.metrics.compute_acc import th_accuracy
+from funasr.utils.datadir_writer import DatadirWriter
+from funasr.models.sanm_kws.model import SanmKWS
+from funasr.models.paraformer.search import Hypothesis
+from funasr.models.paraformer.cif_predictor import mae_loss
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+
+
+if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
+    from torch.cuda.amp import autocast
+else:
+    # Nothing to do if torch<1.6.0
+    @contextmanager
+    def autocast(enabled=True):
+        """Autocast.
+        
+            Args:
+                enabled: TODO.
+            """
+        yield
+
+
+@tables.register("model_classes", "SanmKWSStreaming")
+class SanmKWSStreaming(SanmKWS):
+    """
+    Author: Speech Lab of DAMO Academy, Alibaba Group
+    Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
+    https://arxiv.org/abs/2206.08317
+    """
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        """Initialize SanmKWSStreaming.
+        
+            Args:
+                *args: Variable positional arguments.
+                **kwargs: Additional keyword arguments.
+            """
+        super().__init__(*args, **kwargs)
+
+    def forward(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        text: torch.Tensor,
+        text_lengths: torch.Tensor,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+        """Encoder + Decoder + Calc loss
+        Args:
+                speech: (Batch, Length, ...)
+                speech_lengths: (Batch, )
+                text: (Batch, Length)
+                text_lengths: (Batch,)
+        """
+        decoding_ind = kwargs.get("decoding_ind")
+        if len(text_lengths.size()) > 1:
+            text_lengths = text_lengths[:, 0]
+        if len(speech_lengths.size()) > 1:
+            speech_lengths = speech_lengths[:, 0]
+
+        batch_size = speech.shape[0]
+
+        # Encoder
+        if hasattr(self.encoder, "overlap_chunk_cls"):
+            ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
+            encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
+        else:
+            encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+
+        # decoder: CTC branch
+        if hasattr(self.encoder, "overlap_chunk_cls"):
+            encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(
+                encoder_out, encoder_out_lens, chunk_outs=None
+            )
+        else:
+            encoder_out_ctc, encoder_out_lens_ctc = encoder_out, encoder_out_lens
+
+        loss_ctc, cer_ctc = self._calc_ctc_loss(
+            encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
+        )
+
+        # Collect CTC branch stats
+        stats = dict()
+        stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
+        stats["cer_ctc"] = cer_ctc
+
+        loss = loss_ctc
+
+        stats["cer"] = cer_ctc
+        stats["loss"] = torch.clone(loss.detach())
+
+        # force_gatherable: to-device and to-tensor if scalar for DataParallel
+        loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+        return loss, stats, weight
+
+    def encode_chunk(
+        self,
+        speech: torch.Tensor,
+        speech_lengths: torch.Tensor,
+        cache: dict = None,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Encode chunk.
+        
+            Args:
+                speech: Speech audio tensor, shape (batch, time).
+                speech_lengths: Length of each speech sample.
+                cache: State cache dict for streaming inference.
+                **kwargs: Additional keyword arguments.
+            """
+        if cache is None:
+            cache = {}
+        """Frontend + Encoder. Note that this method is used by asr_inference.py
+        Args:
+                speech: (Batch, Length, ...)
+                speech_lengths: (Batch, )
+                ind: int
+        """
+        with autocast(False):
+            # Data augmentation
+            if self.specaug is not None and self.training:
+                speech, speech_lengths = self.specaug(speech, speech_lengths)
+
+            # Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
+            if self.normalize is not None:
+                speech, speech_lengths = self.normalize(speech, speech_lengths)
+
+        # Forward encoder
+        encoder_out, encoder_out_lens, _ = self.encoder.forward_chunk(
+            speech, speech_lengths, cache=cache["encoder"]
+        )
+
+        if isinstance(encoder_out, tuple):
+            encoder_out = encoder_out[0]
+
+        return encoder_out, torch.tensor([encoder_out.size(1)])
+
+    def init_cache(self, cache: dict = None, **kwargs):
+        """Init cache.
+        
+            Args:
+                cache: State cache dict for streaming inference.
+                **kwargs: Additional keyword arguments.
+            """
+        if cache is None:
+            cache = {}
+        chunk_size = kwargs.get("chunk_size", [0, 10, 5])
+        encoder_chunk_look_back = kwargs.get("encoder_chunk_look_back", 0)
+        decoder_chunk_look_back = kwargs.get("decoder_chunk_look_back", 0)
+        batch_size = 1
+
+        enc_output_size = kwargs["encoder_conf"]["output_size"]
+        feats_dims = kwargs["frontend_conf"]["n_mels"] * kwargs["frontend_conf"]["lfr_m"]
+        cache_encoder = {
+            "start_idx": 0,
+            "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
+            "cif_alphas": torch.zeros((batch_size, 1)),
+            "encoder_out": None,
+            "encoder_out_lens": None,
+            "chunk_size": chunk_size,
+            "encoder_chunk_look_back": encoder_chunk_look_back,
+            "last_chunk": False,
+            "opt": None,
+            "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)),
+            "tail_chunk": False,
+        }
+        cache["encoder"] = cache_encoder
+
+        cache_decoder = {
+            "decode_fsmn": None,
+            "decoder_chunk_look_back": decoder_chunk_look_back,
+            "opt": None,
+            "chunk_size": chunk_size,
+        }
+        cache["decoder"] = cache_decoder
+        cache["frontend"] = {}
+        cache["prev_samples"] = torch.empty(0)
+
+        return cache
+
+    def generate_chunk(
+        self,
+        speech,
+        speech_lengths=None,
+        key: list = None,
+        tokenizer=None,
+        frontend=None,
+        **kwargs,
+    ):
+        """Generate chunk.
+        
+            Args:
+                speech: Speech audio tensor, shape (batch, time).
+                speech_lengths: Length of each speech sample.
+                key: Sample identifiers.
+                tokenizer: Tokenizer instance for text encoding/decoding.
+                frontend: Audio frontend for feature extraction.
+                **kwargs: Additional keyword arguments.
+            """
+        cache = kwargs.get("cache", {})
+        speech = speech.to(device=kwargs["device"])
+        speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+        # Encoder
+        is_final = kwargs.get("is_final", False)
+        encoder_out, encoder_out_lens = self.encode_chunk(
+            speech, speech_lengths, cache=cache, is_final=is_final
+        )
+        if isinstance(encoder_out, tuple):
+            encoder_out = encoder_out[0]
+
+        chunk_size = cache["encoder"]["chunk_size"]
+        real_start_pos = chunk_size[0]
+
+        if encoder_out_lens[0] > chunk_size[0] + chunk_size[1] + chunk_size[2]:
+            assert False, print("impossible case 1 !")
+        if encoder_out_lens[0] == chunk_size[0] + chunk_size[1] + chunk_size[2]:
+            real_end_pos = chunk_size[0] + chunk_size[1]
+        elif encoder_out_lens[0] > chunk_size[0] + chunk_size[1]:
+            real_end_pos = chunk_size[0] + chunk_size[1]
+        elif encoder_out_lens[0] > chunk_size[0]:
+            real_end_pos = encoder_out_lens[0]
+        else:
+            assert False, print("impossible case 2 !")
+
+        encoder_out_accum = cache["encoder"]["encoder_out"]
+        if encoder_out_accum is not None:
+            encoder_out_accum = torch.cat((encoder_out_accum, encoder_out[:, real_start_pos:real_end_pos, :]), dim=1)
+        else:
+            encoder_out_accum = encoder_out[:, real_start_pos:real_end_pos, :]
+        cache["encoder"]["encoder_out"] = encoder_out_accum
+
+        if cache["encoder"]["encoder_out_lens"] is not None:
+            cache["encoder"]["encoder_out_lens"][0] += real_end_pos - real_start_pos
+        else:
+            cache["encoder"]["encoder_out_lens"] = encoder_out_lens
+            cache["encoder"]["encoder_out_lens"][0] = real_end_pos - real_start_pos
+
+        if is_final:
+            if kwargs.get("output_dir") is not None:
+                if not hasattr(self, "writer"):
+                    self.writer = DatadirWriter(kwargs.get("output_dir"))
+
+            results = []
+            for i in range(encoder_out_accum.size(0)):
+                x = encoder_out_accum[i, : cache["encoder"]["encoder_out_lens"][i], :]
+                detect_result = self.kws_decoder.decode(x)
+                is_deted, det_keyword, det_score = detect_result[0], detect_result[1], detect_result[2]
+
+                if is_deted:
+                    self.writer["detect"][key[i]] = "detected " + det_keyword + " " + str(det_score)
+                    det_info = "detected " + det_keyword + " " + str(det_score)
+                else:
+                    self.writer["detect"][key[i]] = "rejected"
+                    det_info = "rejected"
+
+                result_i = {"key": key[i], "text": det_info}
+                results.append(result_i)
+
+            return results
+        else:
+            return None
+
+    def inference(
+        self,
+        data_in,
+        data_lengths=None,
+        key: list = None,
+        tokenizer=None,
+        frontend=None,
+        cache: dict = None,
+        **kwargs,
+    ):
+        """Run inference on input data.
+        
+            Args:
+                data_in: Input data (audio samples, file paths, or text).
+                data_lengths: Lengths of each input sample in the batch.
+                key: Sample identifiers.
+                tokenizer: Tokenizer instance for text encoding/decoding.
+                frontend: Audio frontend for feature extraction.
+                cache: State cache dict for streaming inference.
+                **kwargs: Additional keyword arguments.
+            """
+        if cache is None:
+            cache = {}
+        keywords = kwargs.get("keywords")
+        from funasr.utils.kws_utils import KwsCtcPrefixDecoder
+        self.kws_decoder = KwsCtcPrefixDecoder(
+            ctc=self.ctc,
+            keywords=keywords,
+            token_list=tokenizer.token_list,
+            seg_dict=tokenizer.seg_dict,
+        )
+
+        meta_data = {}
+        chunk_size = kwargs["chunk_size"]
+        chunk_stride_samples = int(chunk_size[1] * 960)  # 600ms
+        first_chunk_padding_samples = int(chunk_size[2] * 960)  # 600ms
+
+        if len(cache) == 0:
+            self.init_cache(cache, **kwargs)
+
+        time1 = time.perf_counter()
+        cfg = {"is_final": kwargs.get("is_final", False)}
+        audio_sample_list = load_audio_text_image_video(
+            data_in,
+            fs=frontend.fs,
+            audio_fs=kwargs.get("fs", 16000),
+            data_type=kwargs.get("data_type", "sound"),
+            tokenizer=tokenizer,
+            cache=cfg,
+        )
+        _is_final = cfg["is_final"]  # if data_in is a file or url, set is_final=True
+
+        time2 = time.perf_counter()
+        meta_data["load_data"] = f"{time2 - time1:0.3f}"
+        assert len(audio_sample_list) == 1, "batch_size must be set 1"
+
+        audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
+
+        if len(audio_sample) < first_chunk_padding_samples:
+            print("key: {}, audio is too short for inference {}".format(key, len(audio_sample)))
+
+        audio_sample_pre = audio_sample[0 : first_chunk_padding_samples]
+        feat_pre, feat_pre_lengths = extract_fbank(
+            [audio_sample_pre],
+            data_type=kwargs.get("data_type", "sound"),
+            frontend=frontend,
+            cache=cache["frontend"],
+            is_final=False,
+        )
+
+        audio_sample = audio_sample[first_chunk_padding_samples:]
+        audio_chunks = int(len(audio_sample) // chunk_stride_samples)
+
+        for i in range(audio_chunks):
+            if i == 0:
+                cache["encoder"]["feats"][:, chunk_size[2] :, :] = feat_pre
+
+            kwargs["is_final"] = False
+            audio_sample_i = audio_sample[i * chunk_stride_samples : (i + 1) * chunk_stride_samples]
+
+            if kwargs["is_final"] and len(audio_sample_i) < 960:
+                cache["encoder"]["tail_chunk"] = True
+                speech = cache["encoder"]["feats"]
+                speech_lengths = torch.tensor([speech.shape[1]], dtype=torch.int64).to(
+                    speech.device
+                )
+            else:
+                # extract fbank feats
+                speech, speech_lengths = extract_fbank(
+                    [audio_sample_i],
+                    data_type=kwargs.get("data_type", "sound"),
+                    frontend=frontend,
+                    cache=cache["frontend"],
+                    is_final=kwargs["is_final"],
+                )
+            time3 = time.perf_counter()
+            meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+            meta_data["batch_data_time"] = (
+                speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
+            )
+
+            results_chunk_i = self.generate_chunk(
+                speech,
+                speech_lengths,
+                key=key,
+                tokenizer=tokenizer,
+                cache=cache,
+                frontend=frontend,
+                **kwargs,
+            )
+
+            # results_chunk_i must be None when is_final=False
+            assert results_chunk_i is None
+
+        # process tail samples
+        tail_audio_sample = audio_sample[ audio_chunks * chunk_stride_samples: ]
+        if len(tail_audio_sample) < 960:
+            kwargs["is_final"] = _is_final
+            cache["encoder"]["tail_chunk"] = True
+            speech = cache["encoder"]["feats"]
+            speech_lengths = torch.tensor([speech.shape[1]], dtype=torch.int64).to(
+                speech.device
+            )
+            results_chunk_tail = self.generate_chunk(
+                speech,
+                speech_lengths,
+                key=key,
+                tokenizer=tokenizer,
+                cache=cache,
+                frontend=frontend,
+                **kwargs,
+            )
+        elif len(tail_audio_sample) <= first_chunk_padding_samples:
+            kwargs["is_final"] = _is_final
+            # extract fbank feats
+            # cache["encoder"]["tail_chunk"] = True  # cannot be true
+            speech, speech_lengths = extract_fbank(
+                [ tail_audio_sample ],
+                data_type=kwargs.get("data_type", "sound"),
+                frontend=frontend,
+                cache=cache["frontend"],
+                is_final=kwargs["is_final"],
+            )
+            results_chunk_tail = self.generate_chunk(
+                speech,
+                speech_lengths,
+                key=key,
+                tokenizer=tokenizer,
+                cache=cache,
+                frontend=frontend,
+                **kwargs,
+            )
+        elif len(tail_audio_sample) > first_chunk_padding_samples and \
+             len(tail_audio_sample) < chunk_stride_samples:
+            kwargs["is_final"] = False
+            # extract fbank feats
+            speech, speech_lengths = extract_fbank(
+                [ tail_audio_sample ],
+                data_type=kwargs.get("data_type", "sound"),
+                frontend=frontend,
+                cache=cache["frontend"],
+                is_final=kwargs["is_final"],
+            )
+            results_chunk = self.generate_chunk(
+                speech,
+                speech_lengths,
+                key=key,
+                tokenizer=tokenizer,
+                cache=cache,
+                frontend=frontend,
+                **kwargs,
+            )
+            # results_chunk must be None when is_final=False
+            assert results_chunk is None
+
+            # push tail chunk
+            kwargs["is_final"] = _is_final
+            cache["encoder"]["tail_chunk"] = True
+            speech = cache["encoder"]["feats"]
+            speech_lengths = torch.tensor([speech.shape[1]], dtype=torch.int64).to(
+                speech.device
+            )
+            results_chunk_tail = self.generate_chunk(
+                speech,
+                speech_lengths,
+                key=key,
+                tokenizer=tokenizer,
+                cache=cache,
+                frontend=frontend,
+                **kwargs,
+            )
+
+        result = results_chunk_tail
+
+        if _is_final:
+            self.init_cache(cache, **kwargs)
+
+        if kwargs.get("output_dir"):
+            if not hasattr(self, "writer"):
+                self.writer = DatadirWriter(kwargs.get("output_dir"))
+
+        return result, meta_data
+
+    def export(self, **kwargs):
+        """Export.
+        
+            Args:
+                **kwargs: Additional keyword arguments.
+            """
+        from .export_meta import export_rebuild_model
+
+        models = export_rebuild_model(model=self, **kwargs)
+        return models