update with main (#1786)

zhifu gao

2024-06-06 32e783664534bbb8d3b8ba64c2c2ecb42398eb00

update with main (#1786)

* 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

* docs

* docs

* docs

* 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

* 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

---------

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>

 funasr/auto/auto_frontend.py

@@ -60,7 +60,7 @@

        result_list = []
        num_samples = len(data_list)
        pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)
        # pbar = tqdm(colour="blue", total=num_samples + 1, dynamic_ncols=True)

        time0 = time.perf_counter()
        for beg_idx in range(0, num_samples, batch_size):
@@ -95,15 +95,15 @@
                "input": speech,
                "input_len": speech_lengths,
                "key": key_batch,
                data_type: "fbank",
                "data_type": "fbank",
            }
            result_list.append(batch)

            pbar.update(1)
            description = f"{meta_data}, "
            pbar.set_description(description)
            # pbar.update(1)
            # description = f"{meta_data}, "
            # pbar.set_description(description)

        time_end = time.perf_counter()
        pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")
        # pbar.set_description(f"time escaped total: {time_end - time0:0.3f}")

        return result_list

 funasr/datasets/audio_datasets/espnet_samplers.py

@@ -147,7 +147,9 @@
        start_idx = self.rank * batches_per_rank
        end_idx = start_idx + batches_per_rank
        rank_batches = buffer_batches[start_idx + self.start_step : end_idx]

        self.batch_num = len(rank_batches)

        logging.info(
            f"rank: {self.rank}, dataloader start from step: {self.start_step}, batch_num: {end_idx-start_idx}, batch_num_after_step: {len(rank_batches)}"
        )

 funasr/models/llm_asr/adaptor.py

@@ -1,5 +1,7 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from funasr.models.transformer.utils.nets_utils import make_pad_mask

from funasr.register import tables

@@ -63,3 +65,64 @@
        query_proj = self.norm(self.linear(query_output.last_hidden_state))

        return query_proj


@tables.register("adaptor_classes", "Transformer")
class Transformer(nn.Module):
    def __init__(
        self, downsample_rate=2, encoder_dim=1280, llm_dim=4096, ffn_dim: int = 2048, **kwargs
    ):
        super().__init__()
        self.k = downsample_rate
        self.encoder_dim = encoder_dim
        self.llm_dim = llm_dim
        self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(ffn_dim, self.llm_dim)
        from funasr.models.transformer.encoder import EncoderLayer
        from funasr.models.transformer.attention import MultiHeadedAttention
        from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward

        self.blocks = nn.ModuleList(
            [
                EncoderLayer(
                    llm_dim,
                    MultiHeadedAttention(
                        kwargs.get("attention_heads", 8),
                        llm_dim,
                        kwargs.get("attention_dropout_rate", 0.0),
                    ),
                    PositionwiseFeedForward(
                        llm_dim,
                        llm_dim // 4,
                        kwargs.get("dropout_rate", 0.0),
                    ),
                    kwargs.get("dropout_rate", 0.0),
                )
                for i in range(kwargs.get("n_layer", 2))
            ]
        )

    def forward(self, x, ilens=None):

        batch_size, seq_len, dim = x.size()
        # num_frames_to_discard = seq_len % self.k
        chunk_num = (seq_len - 1) // self.k + 1
        pad_num = chunk_num * self.k - seq_len
        x = F.pad(x, (0, 0, 0, pad_num, 0, 0), value=0.0)
        # if num_frames_to_discard > 0:
        #     x = x[:, :-num_frames_to_discard, :]
        seq_len = x.size(1)

        x = x.contiguous()
        x = x.view(batch_size, chunk_num, dim * self.k)
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)

        olens = None
        olens = (ilens - 1) // self.k + 1
        masks = (~make_pad_mask(olens)[:, None, :]).to(x.device)
        for layer, block in enumerate(self.blocks):
            x, masks = block(x, masks)
        return x, olens

 funasr/models/sense_voice/decoder.py

@@ -360,6 +360,7 @@
        """Score."""
        ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
        logp = self.forward(ys.unsqueeze(0), x.unsqueeze(0), cache=state)
        logp = torch.log_softmax(logp, dim=-1)
        return logp.squeeze(0)[-1, :], state



 funasr/models/sense_voice/model.py

@@ -1264,6 +1264,9 @@
        if isinstance(task, str):
            task = [task]
        task = "".join([f"<|{x}|>" for x in task])
        
        sos = kwargs.get("model_conf").get("sos")
        if isinstance(sos, str):
        initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")

        language = DecodingOptions.get("language", None)
@@ -1271,8 +1274,19 @@

        sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
        sos_int = tokenizer.encode(sos, allowed_special="all")
        else:
            language = DecodingOptions.get("language", None)
            language = None if language == "auto" else language
            initial_prompt = kwargs.get("initial_prompt", f"{task}")
            initial_prompt_lid = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
            initial_prompt_lid_int = tokenizer.encode(initial_prompt_lid, allowed_special="all")
            sos_int = [sos] + initial_prompt_lid_int
        eos = kwargs.get("model_conf").get("eos")
        if isinstance(eos, str):
        eos_int = tokenizer.encode(eos, allowed_special="all")
        else:
            eos_int = [eos]

        self.beam_search.sos = sos_int
        self.beam_search.eos = eos_int[0]

@@ -1298,7 +1312,7 @@
        self.beam_search.event_score_ga = DecodingOptions.get("gain_tokens_score", [1, 1, 1, 1])

        encoder_out, encoder_out_lens = self.encode(
            speech[None, :, :].permute(0, 2, 1), speech_lengths
            speech[None, :, :], speech_lengths
        )

        if text_token_int is not None:

 funasr/models/sense_voice/whisper_lib/model.py

@@ -27,9 +27,24 @@
    n_text_layer: int


# class LayerNorm(nn.LayerNorm):
#     def forward(self, x: Tensor) -> Tensor:
#         return super().forward(x.float()).type(x.dtype)


class LayerNorm(nn.LayerNorm):
    def forward(self, x: Tensor) -> Tensor:
        return super().forward(x.float()).type(x.dtype)
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def forward(self, input):
        output = F.layer_norm(
            input.float(),
            self.normalized_shape,
            self.weight.float() if self.weight is not None else None,
            self.bias.float() if self.bias is not None else None,
            self.eps,
        )
        return output.type_as(input)


class Linear(nn.Linear):

 funasr/models/transformer/encoder.py

@@ -64,7 +64,7 @@
        stochastic_depth_rate=0.0,
    ):
        """Construct an EncoderLayer object."""
        super(EncoderLayer, self).__init__()
        super().__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.norm1 = LayerNorm(size)

 funasr/train_utils/trainer_ds.py

@@ -621,7 +621,6 @@
            self.train_acc_avg = train_acc_avg.detach().cpu().item() / self.world_size

    def forward_step(self, model, batch, loss_dict={}):
        dtype = torch.bfloat16
        with maybe_autocast(dtype=self.dtype, use_deepspeed=self.use_deepspeed):
            retval = model(**batch)