update with main (#1817)

* add cmakelist * add paraformer-torch * add debug for funasr-onnx-offline * fix redefinition of jieba StdExtension.hpp * add loading torch models * update funasr-onnx-offline * add SwitchArg for wss-server * add SwitchArg for funasr-onnx-offline * update cmakelist * update funasr-onnx-offline-rtf * add define condition * add gpu define for offlne-stream * update com define * update offline-stream * update cmakelist * update func CompileHotwordEmbedding * add timestamp for paraformer-torch * add C10_USE_GLOG for paraformer-torch * update paraformer-torch * fix func FunASRWfstDecoderInit * update model.h * fix func FunASRWfstDecoderInit * fix tpass_stream * update paraformer-torch * add bladedisc for funasr-onnx-offline * update comdefine * update funasr-wss-server * add log for torch * fix GetValue BLADEDISC * fix log * update cmakelist * update warmup to 10 * update funasrruntime * add batch_size for wss-server * add batch for bins * add batch for offline-stream * add batch for paraformer * add batch for offline-stream * fix func SetBatchSize * add SetBatchSize for model * add SetBatchSize for model * fix func Forward * fix padding * update funasrruntime * add dec reset for batch * set batch default value * add argv for CutSplit * sort frame_queue * sorted msgs * fix FunOfflineInfer * add dynamic batch for fetch * fix FetchDynamic * update run_server.sh * update run_server.sh * cpp http post server support (#1739) * add cpp http server * add some comment * remove some comments * del debug infos * restore run_server.sh * adapt to new model struct * 修复了onnxruntime在macos下编译失败的错误 (#1748) * Add files via upload 增加macos的编译支持 * Add files via upload 增加macos支持 * Add files via upload target_link_directories(funasr PUBLIC ${ONNXRUNTIME_DIR}/lib) target_link_directories(funasr PUBLIC ${FFMPEG_DIR}/lib) 添加 if(APPLE) 限制 --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> * Delete docs/images/wechat.png * Add files via upload * fixed the issues about seaco-onnx timestamp * fix bug (#1764) 当语音识别结果包含 `http` 时，标点符号预测会把它会被当成 url * fix empty asr result (#1765) 解码结果为空的语音片段，text 用空字符串 * update export * update export * docs * docs * update export name * docs * update * docs * docs * keep empty speech result (#1772) * docs * docs * update wechat QRcode * Add python funasr api support for websocket srv (#1777) * add python funasr_api supoort * change little to README.md * add core tools stream * modified a little * fix bug for timeout * support for buffer decode * add ffmpeg decode for buffer * libtorch demo * update libtorch infer * update utils * update demo * update demo * update libtorch inference * update model class * update seaco paraformer * bug fix * bug fix * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * auto frontend * Dev gzf exp (#1785) * resume from step * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * batch * train_loss_avg train_acc_avg * train_loss_avg train_acc_avg * train_loss_avg train_acc_avg * log step * wav is not exist * wav is not exist * decoding * decoding * decoding * wechat * decoding key * decoding key * decoding key * decoding key * decoding key * decoding key * dynamic batch * start_data_split_i=0 * total_time/accum_grad * total_time/accum_grad * total_time/accum_grad * update avg slice * update avg slice * sensevoice sanm * sensevoice sanm * sensevoice sanm --------- Co-authored-by: 北念 <lzr265946@alibaba-inc.com> * auto frontend * update paraformer timestamp * [Optimization] support bladedisc fp16 optimization (#1790) * add cif_v1 and cif_export * Update SDK_advanced_guide_offline_zh.md * add cif_wo_hidden_v1 * [fix] fix empty asr result (#1794) * english timestamp for valilla paraformer * wechat * [fix] better solution for handling empty result (#1796) * update scripts * modify the qformer adaptor (#1804) Co-authored-by: nichongjia-2007 <nichongjia@gmail.com> * add ctc inference code (#1806) Co-authored-by: haoneng.lhn <haoneng.lhn@alibaba-inc.com> * Update auto_model.py 修复空字串进入speaker model时报raw_text变量不存在的bug * Update auto_model.py 修复识别出空串后spk_model内变量未定义问题 * update model name * fix paramter 'quantize' unused issue (#1813) Co-authored-by: ZihanLiao <liaozihan1@xdf.cn> * wechat * Update cif_predictor.py (#1811) * Update cif_predictor.py * modify cif_v1_export under extreme cases, max_label_len calculated by batch_len misaligns with token_num * Update cif_predictor.py torch.cumsum precision degradation, using float64 instead * update code --------- Co-authored-by: 雾聪 <wucong.lyb@alibaba-inc.com> Co-authored-by: zhaomingwork <61895407+zhaomingwork@users.noreply.github.com> Co-authored-by: szsteven008 <97944818+szsteven008@users.noreply.github.com> Co-authored-by: Ephemeroptera <605686962@qq.com> Co-authored-by: 彭震东 <zhendong.peng@qq.com> Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com> Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> Co-authored-by: 北念 <lzr265946@alibaba-inc.com> Co-authored-by: xiaowan0322 <wanchen.swc@alibaba-inc.com> Co-authored-by: zhuangzhong <zhuangzhong@corp.netease.com> Co-authored-by: Xingchen Song(宋星辰) <xingchensong1996@163.com> Co-authored-by: nichongjia-2007 <nichongjia@gmail.com> Co-authored-by: haoneng.lhn <haoneng.lhn@alibaba-inc.com> Co-authored-by: liugz18 <57401541+liugz18@users.noreply.github.com> Co-authored-by: Marlowe <54339989+ZihanLiao@users.noreply.github.com> Co-authored-by: ZihanLiao <liaozihan1@xdf.cn> Co-authored-by: zhong zhuang <zhuangz@lamda.nju.edu.cn>
modelscope · Jun 19, 2024 · ad99b26 · ad99b26
1 parent de0b35b
commit ad99b26
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diff --git a/docs/images/wechat.png b/docs/images/wechat.png
diff --git a/examples/industrial_data_pretraining/bicif_paraformer/export.py b/examples/industrial_data_pretraining/bicif_paraformer/export.py
@@ -12,17 +12,17 @@
     device="cpu",
 )
 
-res = model.export(type="onnx", quantize=False)
+res = model.export(type="torchscripts", quantize=False)
 print(res)
 
 
-# method2, inference from local path
-from funasr import AutoModel
+# # method2, inference from local path
+# from funasr import AutoModel
 
-model = AutoModel(
-    model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-    device="cpu",
-)
+# model = AutoModel(
+#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+#     device="cpu",
+# )
 
-res = model.export(type="onnx", quantize=False)
-print(res)
+# res = model.export(type="onnx", quantize=False)
+# print(res)
diff --git a/examples/industrial_data_pretraining/ctc/demo.py b/examples/industrial_data_pretraining/ctc/demo.py
@@ -6,6 +6,7 @@
 import sys
 from funasr import AutoModel
 
+
 model_dir = "/Users/zhifu/Downloads/modelscope_models/ctc_model"
 input_file = (
     "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav"

diff --git a/examples/industrial_data_pretraining/paraformer/export.py b/examples/industrial_data_pretraining/paraformer/export.py
@@ -10,19 +10,20 @@
 from funasr import AutoModel
 
 model = AutoModel(
-    model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+    model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
 )
 
-res = model.export(type="onnx", quantize=False)
+res = model.export(type="torchscripts", quantize=False)
+# res = model.export(type="bladedisc", input=f"{model.model_path}/example/asr_example.wav")
 print(res)
 
 
-# method2, inference from local path
-from funasr import AutoModel
+# # method2, inference from local path
+# from funasr import AutoModel
 
-model = AutoModel(
-    model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-)
+# model = AutoModel(
+#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+# )
 
-res = model.export(type="onnx", quantize=False)
-print(res)
+# res = model.export(type="onnx", quantize=False)
+# print(res)
diff --git a/funasr/auto/auto_model.py b/funasr/auto/auto_model.py
@@ -324,7 +324,7 @@ def inference_with_vad(self, input, input_len=None, **cfg):
             input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg
         )
         end_vad = time.time()
-            
+
         #  FIX(gcf): concat the vad clips for sense vocie model for better aed
         if kwargs.get("merge_vad", False):
             for i in range(len(res)):
@@ -467,23 +467,20 @@ def inference_with_vad(self, input, input_len=None, **cfg):
                         else:
                             result[k] += restored_data[j][k]
 
+            if not len(result["text"].strip()):
+                continue
             return_raw_text = kwargs.get("return_raw_text", False)
             # step.3 compute punc model
+            raw_text = None
             if self.punc_model is not None:
-                if not len(result["text"].strip()):
-                    if return_raw_text:
-                        result["raw_text"] = ""
-                else:
-                    deep_update(self.punc_kwargs, cfg)
-                    punc_res = self.inference(
-                        result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg
-                    )
-                    raw_text = copy.copy(result["text"])
-                    if return_raw_text:
-                        result["raw_text"] = raw_text
-                    result["text"] = punc_res[0]["text"]
-            else:
-                raw_text = None
+                deep_update(self.punc_kwargs, cfg)
+                punc_res = self.inference(
+                    result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg
+                )
+                raw_text = copy.copy(result["text"])
+                if return_raw_text:
+                    result["raw_text"] = raw_text
+                result["text"] = punc_res[0]["text"]
 
             # speaker embedding cluster after resorted
             if self.spk_model is not None and kwargs.get("return_spk_res", True):
@@ -605,12 +602,6 @@ def export(self, input=None, **cfg):
         )
 
         with torch.no_grad():
-
-            if type == "onnx":
-                export_dir = export_utils.export_onnx(model=model, data_in=data_list, **kwargs)
-            else:
-                export_dir = export_utils.export_torchscripts(
-                    model=model, data_in=data_list, **kwargs
-                )
+            export_dir = export_utils.export(model=model, data_in=data_list, **kwargs)
 
         return export_dir
diff --git a/funasr/datasets/llm_datasets/datasets.py b/funasr/datasets/llm_datasets/datasets.py
@@ -64,8 +64,6 @@ def __len__(self):
 
     def __getitem__(self, index):
         item = self.index_ds[index]
-        # import pdb;
-        # pdb.set_trace()
         source = item["source"]
         data_src = load_audio_text_image_video(source, fs=self.fs)
         if self.preprocessor_speech:

diff --git a/funasr/datasets/llm_datasets_qwenaudio/datasets.py b/funasr/datasets/llm_datasets_qwenaudio/datasets.py
@@ -66,8 +66,6 @@ def __len__(self):
 
     def __getitem__(self, index):
         item = self.index_ds[index]
-        # import pdb;
-        # pdb.set_trace()
         source = item["source"]
         data_src = load_audio_text_image_video(source, fs=self.fs)
         if self.preprocessor_speech:

diff --git a/funasr/datasets/llm_datasets_vicuna/datasets.py b/funasr/datasets/llm_datasets_vicuna/datasets.py
@@ -66,8 +66,6 @@ def __len__(self):
 
     def __getitem__(self, index):
         item = self.index_ds[index]
-        # import pdb;
-        # pdb.set_trace()
         source = item["source"]
         data_src = load_audio_text_image_video(source, fs=self.fs)
         if self.preprocessor_speech:

diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -72,8 +72,6 @@ def __len__(self):
         return len(self.index_ds)
 
     def __getitem__(self, index):
-        # import pdb;
-        # pdb.set_trace()
 
         output = None
         for idx in range(self.retry):

diff --git a/funasr/frontends/default.py b/funasr/frontends/default.py
@@ -235,7 +235,6 @@ def forward(
         self, input: torch.Tensor, input_lengths: torch.Tensor
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         # 1. Domain-conversion: e.g. Stft: time -> time-freq
-        # import pdb;pdb.set_trace()
         if self.stft is not None:
             input_stft, feats_lens = self._compute_stft(input, input_lengths)
         else:

diff --git a/funasr/models/bicif_paraformer/cif_predictor.py b/funasr/models/bicif_paraformer/cif_predictor.py
@@ -198,7 +198,7 @@ def forward(
             output2 = self.upsample_cnn(_output)
             output2 = output2.transpose(1, 2)
             output2, _ = self.self_attn(output2, mask)
-        # import pdb; pdb.set_trace()
+
         alphas2 = torch.sigmoid(self.cif_output2(output2))
         alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
         # repeat the mask in T demension to match the upsampled length

diff --git a/funasr/models/bicif_paraformer/export_meta.py b/funasr/models/bicif_paraformer/export_meta.py
@@ -29,7 +29,8 @@ def export_rebuild_model(model, **kwargs):
     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
 

diff --git a/funasr/models/contextual_paraformer/decoder.py b/funasr/models/contextual_paraformer/decoder.py
@@ -424,7 +424,6 @@ def forward(
         # contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
         contextual_mask = self.make_pad_mask(contextual_length)
         contextual_mask, _ = self.prepare_mask(contextual_mask)
-        # import pdb; pdb.set_trace()
         contextual_mask = contextual_mask.transpose(2, 1).unsqueeze(1)
         cx, tgt_mask, _, _, _ = self.bias_decoder(
             x_self_attn, tgt_mask, bias_embed, memory_mask=contextual_mask

diff --git a/funasr/models/contextual_paraformer/export_meta.py b/funasr/models/contextual_paraformer/export_meta.py
@@ -16,6 +16,21 @@ def __init__(self, model, **kwargs):
         self.embedding = model.bias_embed
         model.bias_encoder.batch_first = False
         self.bias_encoder = model.bias_encoder
+
+    def export_dummy_inputs(self):
+        hotword = torch.tensor(
+            [
+                [10, 11, 12, 13, 14, 10, 11, 12, 13, 14],
+                [100, 101, 0, 0, 0, 0, 0, 0, 0, 0],
+                [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                [10, 11, 12, 13, 14, 10, 11, 12, 13, 14],
+                [100, 101, 0, 0, 0, 0, 0, 0, 0, 0],
+                [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+            ],
+            dtype=torch.int32,
+        )
+        # hotword_length = torch.tensor([10, 2, 1], dtype=torch.int32)
+        return (hotword)
 
 
 def export_rebuild_model(model, **kwargs):
@@ -59,7 +74,9 @@ def export_rebuild_model(model, **kwargs):
     backbone_model.export_dynamic_axes = types.MethodType(
         export_backbone_dynamic_axes, backbone_model
     )
-    backbone_model.export_name = types.MethodType(export_backbone_name, backbone_model)
+
+    embedder_model.export_name = "model_eb"
+    backbone_model.export_name = "model"
 
     return backbone_model, embedder_model
 

diff --git a/funasr/models/lcbnet/model.py b/funasr/models/lcbnet/model.py
@@ -23,8 +23,6 @@
 from funasr.utils.datadir_writer import DatadirWriter
 from funasr.register import tables
 
-import pdb
-
 
 @tables.register("model_classes", "LCBNet")
 class LCBNet(nn.Module):

diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
@@ -168,8 +168,6 @@ def forward(
                 text: (Batch, Length)
                 text_lengths: (Batch,)
         """
-        # import pdb;
-        # pdb.set_trace()
         if len(text_lengths.size()) > 1:
             text_lengths = text_lengths[:, 0]
         if len(speech_lengths.size()) > 1:

diff --git a/funasr/models/llm_asr_nar/model.py b/funasr/models/llm_asr_nar/model.py
@@ -166,8 +166,6 @@ def forward(
                 text: (Batch, Length)
                 text_lengths: (Batch,)
         """
-        # import pdb;
-        # pdb.set_trace()
         if len(text_lengths.size()) > 1:
             text_lengths = text_lengths[:, 0]
         if len(speech_lengths.size()) > 1:

diff --git a/funasr/models/mfcca/mfcca_encoder.py b/funasr/models/mfcca/mfcca_encoder.py
@@ -34,7 +34,6 @@
 from funasr.models.transformer.utils.subsampling import TooShortUttError
 from funasr.models.transformer.utils.subsampling import check_short_utt
 from funasr.models.encoder.abs_encoder import AbsEncoder
-import pdb
 import math
 
 
@@ -363,7 +362,6 @@ def forward(
         t_leng = xs_pad.size(1)
         d_dim = xs_pad.size(2)
         xs_pad = xs_pad.reshape(-1, channel_size, t_leng, d_dim)
-        # pdb.set_trace()
         if channel_size < 8:
             repeat_num = math.ceil(8 / channel_size)
             xs_pad = xs_pad.repeat(1, repeat_num, 1, 1)[:, 0:8, :, :]

diff --git a/funasr/models/paraformer/cif_predictor.py b/funasr/models/paraformer/cif_predictor.py
@@ -494,6 +494,8 @@ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
         token_num_floor = torch.floor(token_num)
 
         return hidden, alphas, token_num_floor
+
+
 @torch.jit.script
 def cif_v1_export(hidden, alphas, threshold: float):
     device = hidden.device
@@ -516,9 +518,7 @@ def cif_v1_export(hidden, alphas, threshold: float):
     fires[fire_idxs] = 1
     fires = fires + prefix_sum - prefix_sum_floor
 
-    prefix_sum_hidden = torch.cumsum(
-        alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1
-    )
+    prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
 
     frames = prefix_sum_hidden[fire_idxs]
     shift_frames = torch.roll(frames, 1, dims=0)
@@ -530,9 +530,7 @@ def cif_v1_export(hidden, alphas, threshold: float):
     shift_frames[shift_batch_idxs] = 0
 
     remains = fires - torch.floor(fires)
-    remain_frames = (
-        remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
-    )
+    remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
 
     shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
     shift_remain_frames[shift_batch_idxs] = 0
@@ -541,14 +539,13 @@ def cif_v1_export(hidden, alphas, threshold: float):
 
     max_label_len = batch_len.max()
 
-    frame_fires = torch.zeros(
-        batch_size, max_label_len, hidden_size, dtype=dtype, device=device
-    )
+    frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
     indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
     frame_fires_idxs = indices < batch_len.unsqueeze(1)
     frame_fires[frame_fires_idxs] = frames
     return frame_fires, fires
 
+
 @torch.jit.script
 def cif_export(hidden, alphas, threshold: float):
     batch_size, len_time, hidden_size = hidden.size()
@@ -692,11 +689,8 @@ def cif_v1(hidden, alphas, threshold):
     device = hidden.device
     dtype = hidden.dtype
     batch_size, len_time, hidden_size = hidden.size()
-    frames = torch.zeros(batch_size, len_time, hidden_size,
-                         dtype=dtype, device=device)
-    prefix_sum_hidden = torch.cumsum(
-        alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1
-    )
+    frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
+    prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
 
     frames = prefix_sum_hidden[fire_idxs]
     shift_frames = torch.roll(frames, 1, dims=0)
@@ -708,10 +702,7 @@ def cif_v1(hidden, alphas, threshold):
     shift_frames[shift_batch_idxs] = 0
 
     remains = fires - torch.floor(fires)
-    remain_frames = (
-        remains[fire_idxs].unsqueeze(-1).tile((1,
-                                               hidden_size)) * hidden[fire_idxs]
-    )
+    remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
 
     shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
     shift_remain_frames[shift_batch_idxs] = 0
@@ -720,9 +711,7 @@ def cif_v1(hidden, alphas, threshold):
 
     max_label_len = batch_len.max()
 
-    frame_fires = torch.zeros(
-        batch_size, max_label_len, hidden_size, dtype=dtype, device=device
-    )
+    frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
     indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
     frame_fires_idxs = indices < batch_len.unsqueeze(1)
     frame_fires[frame_fires_idxs] = frames

diff --git a/funasr/models/paraformer/export_meta.py b/funasr/models/paraformer/export_meta.py
@@ -31,6 +31,7 @@ def export_rebuild_model(model, **kwargs):
     model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
     model.export_name = types.MethodType(export_name, model)
 
+    model.export_name = 'model'
     return model
 
 

diff --git a/funasr/models/paraformer_streaming/model.py b/funasr/models/paraformer_streaming/model.py
@@ -50,8 +50,6 @@ def __init__(
 
         super().__init__(*args, **kwargs)
 
-        # import pdb;
-        # pdb.set_trace()
         self.sampling_ratio = kwargs.get("sampling_ratio", 0.2)
 
         self.scama_mask = None
@@ -83,8 +81,6 @@ def forward(
                 text: (Batch, Length)
                 text_lengths: (Batch,)
         """
-        # import pdb;
-        # pdb.set_trace()
         decoding_ind = kwargs.get("decoding_ind")
         if len(text_lengths.size()) > 1:
             text_lengths = text_lengths[:, 0]

diff --git a/funasr/models/sanm/attention.py b/funasr/models/sanm/attention.py
@@ -780,7 +780,7 @@ def forward_qkv(self, x, memory):
         return q, k, v
 
     def forward_attention(self, value, scores, mask, ret_attn):
-        scores = scores + mask
+        scores = scores + mask.to(scores.device)
 
         self.attn = torch.softmax(scores, dim=-1)
         context_layer = torch.matmul(self.attn, value)  # (batch, head, time1, d_k)