first revise

Dockerfile

@@ -0,0 +1,12 @@
+FROM git.modelhub.org.cn:9443/enginex-metax/maca-c500-pytorch:2.33.0.6-torch2.6-py310-ubuntu24.04-amd64
+
+RUN /opt/conda/bin/pip install funasr modelscope huggingface_hub
+
+RUN chmod 1777 -R /tmp && apt update && apt install -y ffmpeg
+
+WORKDIR /opt/app
+COPY ./ ./
+RUN /opt/conda/bin/pip install -r requirements.txt
+
+EXPOSE 80
+ENTRYPOINT ["python3", "./test_funasr.py"]

Dockerfile.funasr-mr100

@@ -1,20 +0,0 @@
-FROM git.modelhub.org.cn:9443/enginex-iluvatar/mr100_corex:4.3.0
-
-WORKDIR /root
-
-COPY requirements.txt /root
-RUN pip install -r requirements.txt
-
-RUN apt update && apt install -y vim net-tools
-
-RUN pip install funasr==1.2.6 openai-whisper
-
-ADD . /root/
-ADD nltk_data.tar.gz /root/
-RUN tar -xvzf nltk_data.tar.gz
-
-RUN cp ./replaced_files/mr_v100/cif_predictor.py /usr/local/lib/python3.10/site-packages/funasr/models/paraformer/
-
-EXPOSE 80
-ENTRYPOINT ["bash"]
-CMD ["./start_funasr.sh"]

README.md

@@ -1,8 +1,8 @@
-# 天数智芯 智铠100 FunASR
+# 沐曦 MetaX C500 FunASR
 
 ## 镜像构造
 ```shell
-docker build -f ./Dockerfile.funasr-mr100 -t <your_image> .
+docker build -t <built_img> .
 ```
 
 ## 使用说明
@@ -11,9 +11,8 @@ docker build -f ./Dockerfile.funasr-mr100 -t <your_image> .
 1. 本项目中附带上了示例测试数据，音频文件为`lei-jun-test.wav`，音频的识别准确内容文件为`lei-jun.txt`，用户需要准备好相应的ASR模型路径，本例中假设我们已经下载好了SenseVoiceSmall模型存放于/model/SenseVoiceSmall
 2. 在本项目路径下执行以下快速测试命令
 ```shell
-docker run -it \ 
+metax-docker run -it \ 
-    -v /usr/src:/usr/src \
+    --gpus=[0] \
-    -v /lib/modules:/lib/modules --device=/dev/iluvatar0:/dev/iluvatar0 \
     -v $PWD:/tmp/workspace \
     -v /model:/model \
     -e MODEL_DIR=/model/SenseVoiceSmall \
@@ -21,7 +20,7 @@ docker run -it \
     -e ANSWER_FILE=lei-jun.txt \
     -e RESULT_FILE=result.json \
     --cpus=4 --memory=16g \
-    <your_image>
+    <built_img>
 ```
 上述测试指令成功运行将会在terminal中看到对测试音频的识别结果，运行时间以及1-cer效果指标，并且当前文件下会生成一个`result.json`文件记录刚才的测试结果
 
@@ -29,8 +28,8 @@ docker run -it \
 
 用户可使用类似上述的docker run指令以交互形式进入镜像中，主要的测试代码为`test_funasr.py`,用户可自行修改代码中需要测试的模型路径、测试文件路径以及调用funASR逻辑
 
-## 智铠100模型适配情况
+## MetaX C500 模型适配情况
-我们在智铠100上针对funASR部分进行了所有大类的适配，测试方式为在Nvidia A100环境下和智铠100加速卡上对同一段长音频进行语音识别任务，获取运行时间，1-cer指标。运行时都只使用一张显卡
+我们在 MetaX C500 上针对funASR部分进行了所有大类的适配，测试方式为在Nvidia A100环境下和智铠100加速卡上对同一段长音频进行语音识别任务，获取运行时间，1-cer指标。运行时都只使用一张显卡
 
 | 模型大类 | 模型地址 |A100运行时间(秒)|智铠100运行时间(秒)|A100 1-cer|智铠100 1-cer| 备注                           |
 |------|---------------|-----|----|-------|-------|---------------------|

replaced_files/mr_v100/cif_predictor.py

@@ -1,762 +0,0 @@
-#!/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 torch
-import logging
-import numpy as np
-
-from funasr.register import tables
-from funasr.train_utils.device_funcs import to_device
-from funasr.models.transformer.utils.nets_utils import make_pad_mask
-from torch.cuda.amp import autocast
-
-
-@tables.register("predictor_classes", "CifPredictor")
-class CifPredictor(torch.nn.Module):
-    def __init__(
-        self,
-        idim,
-        l_order,
-        r_order,
-        threshold=1.0,
-        dropout=0.1,
-        smooth_factor=1.0,
-        noise_threshold=0,
-        tail_threshold=0.45,
-    ):
-        super().__init__()
-
-        self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
-        self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
-        self.cif_output = torch.nn.Linear(idim, 1)
-        self.dropout = torch.nn.Dropout(p=dropout)
-        self.threshold = threshold
-        self.smooth_factor = smooth_factor
-        self.noise_threshold = noise_threshold
-        self.tail_threshold = tail_threshold
-
-    def forward(
-        self,
-        hidden,
-        target_label=None,
-        mask=None,
-        ignore_id=-1,
-        mask_chunk_predictor=None,
-        target_label_length=None,
-    ):
-
-        with autocast(False):
-            h = hidden
-            context = h.transpose(1, 2)
-            queries = self.pad(context)
-            memory = self.cif_conv1d(queries)
-            output = memory + context
-            output = self.dropout(output)
-            output = output.transpose(1, 2)
-            output = torch.relu(output)
-            output = self.cif_output(output)
-            alphas = torch.sigmoid(output)
-            alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
-            if mask is not None:
-                mask = mask.transpose(-1, -2).float()
-                alphas = alphas * mask
-            if mask_chunk_predictor is not None:
-                alphas = alphas * mask_chunk_predictor
-            alphas = alphas.squeeze(-1)
-            mask = mask.squeeze(-1)
-            if target_label_length is not None:
-                target_length = target_label_length
-            elif target_label is not None:
-                target_length = (target_label != ignore_id).float().sum(-1)
-            else:
-                target_length = None
-            token_num = alphas.sum(-1)
-            if target_length is not None:
-                alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
-            elif self.tail_threshold > 0.0:
-                hidden, alphas, token_num = self.tail_process_fn(
-                    hidden, alphas, token_num, mask=mask
-                )
-
-            acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
-
-            if target_length is None and self.tail_threshold > 0.0:
-                token_num_int = torch.max(token_num).type(torch.int32).item()
-                acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
-
-        return acoustic_embeds, token_num, alphas, cif_peak
-
-    def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
-        b, t, d = hidden.size()
-        tail_threshold = self.tail_threshold
-        if mask is not None:
-            zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
-            ones_t = torch.ones_like(zeros_t)
-            mask_1 = torch.cat([mask, zeros_t], dim=1)
-            mask_2 = torch.cat([ones_t, mask], dim=1)
-            mask = mask_2 - mask_1
-            tail_threshold = mask * tail_threshold
-            alphas = torch.cat([alphas, zeros_t], dim=1)
-            alphas = torch.add(alphas, tail_threshold)
-        else:
-            tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
-            tail_threshold = torch.reshape(tail_threshold, (1, 1))
-            alphas = torch.cat([alphas, tail_threshold], dim=1)
-        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
-        hidden = torch.cat([hidden, zeros], dim=1)
-        token_num = alphas.sum(dim=-1)
-        token_num_floor = torch.floor(token_num)
-
-        return hidden, alphas, token_num_floor
-
-    def gen_frame_alignments(
-        self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None
-    ):
-        batch_size, maximum_length = alphas.size()
-        int_type = torch.int32
-
-        is_training = self.training
-        if is_training:
-            token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
-        else:
-            token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
-
-        max_token_num = torch.max(token_num).item()
-
-        alphas_cumsum = torch.cumsum(alphas, dim=1)
-        alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
-        alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
-
-        index = torch.ones([batch_size, max_token_num], dtype=int_type)
-        index = torch.cumsum(index, dim=1)
-        index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
-
-        index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
-        index_div_bool_zeros = index_div.eq(0)
-        index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
-        index_div_bool_zeros_count = torch.clamp(
-            index_div_bool_zeros_count, 0, encoder_sequence_length.max()
-        )
-        token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device)
-        index_div_bool_zeros_count *= token_num_mask
-
-        index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(
-            1, 1, maximum_length
-        )
-        ones = torch.ones_like(index_div_bool_zeros_count_tile)
-        zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
-        ones = torch.cumsum(ones, dim=2)
-        cond = index_div_bool_zeros_count_tile == ones
-        index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
-
-        index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool)
-        index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type)
-        index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1)
-        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type)
-        predictor_mask = (
-            (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max()))
-            .type(int_type)
-            .to(encoder_sequence_length.device)
-        )
-        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask
-
-        predictor_alignments = index_div_bool_zeros_count_tile_out
-        predictor_alignments_length = predictor_alignments.sum(-1).type(
-            encoder_sequence_length.dtype
-        )
-        return predictor_alignments.detach(), predictor_alignments_length.detach()
-
-
-@tables.register("predictor_classes", "CifPredictorV2")
-class CifPredictorV2(torch.nn.Module):
-    def __init__(
-        self,
-        idim,
-        l_order,
-        r_order,
-        threshold=1.0,
-        dropout=0.1,
-        smooth_factor=1.0,
-        noise_threshold=0,
-        tail_threshold=0.0,
-        tf2torch_tensor_name_prefix_torch="predictor",
-        tf2torch_tensor_name_prefix_tf="seq2seq/cif",
-        tail_mask=True,
-    ):
-        super().__init__()
-
-        self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
-        self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
-        self.cif_output = torch.nn.Linear(idim, 1)
-        self.dropout = torch.nn.Dropout(p=dropout)
-        self.threshold = threshold
-        self.smooth_factor = smooth_factor
-        self.noise_threshold = noise_threshold
-        self.tail_threshold = tail_threshold
-        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
-        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
-        self.tail_mask = tail_mask
-
-    def forward(
-        self,
-        hidden,
-        target_label=None,
-        mask=None,
-        ignore_id=-1,
-        mask_chunk_predictor=None,
-        target_label_length=None,
-    ):
-
-        with autocast(False):
-            h = hidden
-            context = h.transpose(1, 2)
-            queries = self.pad(context)
-            output = torch.relu(self.cif_conv1d(queries))
-            output = output.transpose(1, 2)
-
-            output = self.cif_output(output)
-            alphas = torch.sigmoid(output)
-            alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
-            if mask is not None:
-                mask = mask.transpose(-1, -2).float()
-                alphas = alphas * mask
-            if mask_chunk_predictor is not None:
-                alphas = alphas * mask_chunk_predictor
-            alphas = alphas.squeeze(-1)
-            mask = mask.squeeze(-1)
-            if target_label_length is not None:
-                target_length = target_label_length.squeeze(-1)
-            elif target_label is not None:
-                target_length = (target_label != ignore_id).float().sum(-1)
-            else:
-                target_length = None
-            token_num = alphas.sum(-1)
-            if target_length is not None:
-                alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
-            elif self.tail_threshold > 0.0:
-                if self.tail_mask:
-                    hidden, alphas, token_num = self.tail_process_fn(
-                        hidden, alphas, token_num, mask=mask
-                    )
-                else:
-                    hidden, alphas, token_num = self.tail_process_fn(
-                        hidden, alphas, token_num, mask=None
-                    )
-
-            acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)
-            if target_length is None and self.tail_threshold > 0.0:
-                token_num_int = torch.max(token_num).type(torch.int32).item()
-                acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
-
-        return acoustic_embeds, token_num, alphas, cif_peak
-
-    def forward_chunk(self, hidden, cache=None, **kwargs):
-        is_final = kwargs.get("is_final", False)
-        batch_size, len_time, hidden_size = hidden.shape
-        h = hidden
-        context = h.transpose(1, 2)
-        queries = self.pad(context)
-        output = torch.relu(self.cif_conv1d(queries))
-        output = output.transpose(1, 2)
-        output = self.cif_output(output)
-        alphas = torch.sigmoid(output)
-        alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
-
-        alphas = alphas.squeeze(-1)
-
-        token_length = []
-        list_fires = []
-        list_frames = []
-        cache_alphas = []
-        cache_hiddens = []
-
-        if cache is not None and "chunk_size" in cache:
-            alphas[:, : cache["chunk_size"][0]] = 0.0
-            if not is_final:
-                alphas[:, sum(cache["chunk_size"][:2]) :] = 0.0
-        if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
-            cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
-            cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
-            hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
-            alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
-        if cache is not None and is_final:
-            tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
-            tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
-            tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
-            hidden = torch.cat((hidden, tail_hidden), dim=1)
-            alphas = torch.cat((alphas, tail_alphas), dim=1)
-
-        len_time = alphas.shape[1]
-        for b in range(batch_size):
-            integrate = 0.0
-            frames = torch.zeros((hidden_size), device=hidden.device)
-            list_frame = []
-            list_fire = []
-            for t in range(len_time):
-                alpha = alphas[b][t]
-                if alpha + integrate < self.threshold:
-                    integrate += alpha
-                    list_fire.append(integrate)
-                    frames += alpha * hidden[b][t]
-                else:
-                    frames += (self.threshold - integrate) * hidden[b][t]
-                    list_frame.append(frames)
-                    integrate += alpha
-                    list_fire.append(integrate)
-                    integrate -= self.threshold
-                    frames = integrate * hidden[b][t]
-
-            cache_alphas.append(integrate)
-            if integrate > 0.0:
-                cache_hiddens.append(frames / integrate)
-            else:
-                cache_hiddens.append(frames)
-
-            token_length.append(torch.tensor(len(list_frame), device=alphas.device))
-            list_fires.append(list_fire)
-            list_frames.append(list_frame)
-
-        cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
-        cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
-        cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
-        cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
-
-        max_token_len = max(token_length)
-        if max_token_len == 0:
-            return hidden, torch.stack(token_length, 0), None, None
-        list_ls = []
-        for b in range(batch_size):
-            pad_frames = torch.zeros(
-                (max_token_len - token_length[b], hidden_size), device=alphas.device
-            )
-            if token_length[b] == 0:
-                list_ls.append(pad_frames)
-            else:
-                list_frames[b] = torch.stack(list_frames[b])
-                list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0))
-
-        cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
-        cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
-        cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
-        cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
-        return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None
-
-    def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
-        b, t, d = hidden.size()
-        tail_threshold = self.tail_threshold
-        if mask is not None:
-            zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
-            ones_t = torch.ones_like(zeros_t)
-            mask_1 = torch.cat([mask, zeros_t], dim=1)
-            mask_2 = torch.cat([ones_t, mask], dim=1)
-            mask = mask_2 - mask_1
-            tail_threshold = mask * tail_threshold
-            alphas = torch.cat([alphas, zeros_t], dim=1)
-            alphas = torch.add(alphas, tail_threshold)
-        else:
-            tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
-            tail_threshold = torch.reshape(tail_threshold, (1, 1))
-            if b > 1:
-                alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1)
-            else:
-                alphas = torch.cat([alphas, tail_threshold], dim=1)
-        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
-        hidden = torch.cat([hidden, zeros], dim=1)
-        token_num = alphas.sum(dim=-1)
-        token_num_floor = torch.floor(token_num)
-
-        return hidden, alphas, token_num_floor
-
-    def gen_frame_alignments(
-        self, alphas: torch.Tensor = None, encoder_sequence_length: torch.Tensor = None
-    ):
-        batch_size, maximum_length = alphas.size()
-        int_type = torch.int32
-
-        is_training = self.training
-        if is_training:
-            token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
-        else:
-            token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
-
-        max_token_num = torch.max(token_num).item()
-
-        alphas_cumsum = torch.cumsum(alphas, dim=1)
-        alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
-        alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
-
-        index = torch.ones([batch_size, max_token_num], dtype=int_type)
-        index = torch.cumsum(index, dim=1)
-        index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
-
-        index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
-        index_div_bool_zeros = index_div.eq(0)
-        index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
-        index_div_bool_zeros_count = torch.clamp(
-            index_div_bool_zeros_count, 0, encoder_sequence_length.max()
-        )
-        token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device)
-        index_div_bool_zeros_count *= token_num_mask
-
-        index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(
-            1, 1, maximum_length
-        )
-        ones = torch.ones_like(index_div_bool_zeros_count_tile)
-        zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
-        ones = torch.cumsum(ones, dim=2)
-        cond = index_div_bool_zeros_count_tile == ones
-        index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
-
-        index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool)
-        index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type)
-        index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1)
-        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type)
-        predictor_mask = (
-            (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max()))
-            .type(int_type)
-            .to(encoder_sequence_length.device)
-        )
-        index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask
-
-        predictor_alignments = index_div_bool_zeros_count_tile_out
-        predictor_alignments_length = predictor_alignments.sum(-1).type(
-            encoder_sequence_length.dtype
-        )
-        return predictor_alignments.detach(), predictor_alignments_length.detach()
-
-
-@tables.register("predictor_classes", "CifPredictorV2Export")
-class CifPredictorV2Export(torch.nn.Module):
-    def __init__(self, model, **kwargs):
-        super().__init__()
-
-        self.pad = model.pad
-        self.cif_conv1d = model.cif_conv1d
-        self.cif_output = model.cif_output
-        self.threshold = model.threshold
-        self.smooth_factor = model.smooth_factor
-        self.noise_threshold = model.noise_threshold
-        self.tail_threshold = model.tail_threshold
-
-    def forward(
-        self,
-        hidden: torch.Tensor,
-        mask: torch.Tensor,
-    ):
-        alphas, token_num = self.forward_cnn(hidden, mask)
-        mask = mask.transpose(-1, -2).float()
-        mask = mask.squeeze(-1)
-        hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
-        acoustic_embeds, cif_peak = cif_v1_export(hidden, alphas, self.threshold)
-
-        return acoustic_embeds, token_num, alphas, cif_peak
-
-    def forward_cnn(
-        self,
-        hidden: torch.Tensor,
-        mask: torch.Tensor,
-    ):
-        h = hidden
-        context = h.transpose(1, 2)
-        queries = self.pad(context)
-        output = torch.relu(self.cif_conv1d(queries))
-        output = output.transpose(1, 2)
-
-        output = self.cif_output(output)
-        alphas = torch.sigmoid(output)
-        alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
-        mask = mask.transpose(-1, -2).float()
-        alphas = alphas * mask
-        alphas = alphas.squeeze(-1)
-        token_num = alphas.sum(-1)
-
-        return alphas, token_num
-
-    def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
-        b, t, d = hidden.size()
-        tail_threshold = self.tail_threshold
-
-        zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
-        ones_t = torch.ones_like(zeros_t)
-
-        mask_1 = torch.cat([mask, zeros_t], dim=1)
-        mask_2 = torch.cat([ones_t, mask], dim=1)
-        mask = mask_2 - mask_1
-        tail_threshold = mask * tail_threshold
-        alphas = torch.cat([alphas, zeros_t], dim=1)
-        alphas = torch.add(alphas, tail_threshold)
-
-        zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
-        hidden = torch.cat([hidden, zeros], dim=1)
-        token_num = alphas.sum(dim=-1)
-        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
-    dtype = hidden.dtype
-    batch_size, len_time, hidden_size = hidden.size()
-    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
-
-    frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
-    fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
-
-    # prefix_sum = torch.cumsum(alphas, dim=1)
-    prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
-        torch.float32
-    )  # cumsum precision degradation cause wrong result in extreme
-    prefix_sum_floor = torch.floor(prefix_sum)
-    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
-    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
-
-    dislocation_prefix_sum_floor[:, 0] = 0
-    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor
-
-    fire_idxs = dislocation_diff > 0
-    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).repeat((1, 1, hidden_size)) * hidden, dim=1)
-    frames = prefix_sum_hidden[fire_idxs]
-    shift_frames = torch.roll(frames, 1, dims=0)
-
-    batch_len = fire_idxs.sum(1)
-    batch_idxs = torch.cumsum(batch_len, dim=0)
-    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)
-    shift_batch_idxs[0] = 0
-    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).repeat((1, hidden_size)) * hidden[fire_idxs]
-
-    shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
-    shift_remain_frames[shift_batch_idxs] = 0
-
-    frames = frames - shift_frames + shift_remain_frames - remain_frames
-
-    # max_label_len = batch_len.max()
-    max_label_len = alphas.sum(dim=-1)
-    max_label_len = torch.floor(max_label_len).max().to(dtype=torch.int64)
-
-    # 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()
-    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
-
-    # loop varss
-    integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
-    frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
-    # intermediate vars along time
-    list_fires = []
-    list_frames = []
-
-    for t in range(len_time):
-        alpha = alphas[:, t]
-        distribution_completion = (
-            torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
-        )
-
-        integrate += alpha
-        list_fires.append(integrate)
-
-        fire_place = integrate >= threshold
-        integrate = torch.where(
-            fire_place,
-            integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
-            integrate,
-        )
-        cur = torch.where(fire_place, distribution_completion, alpha)
-        remainds = alpha - cur
-
-        frame += cur[:, None] * hidden[:, t, :]
-        list_frames.append(frame)
-        frame = torch.where(
-            fire_place[:, None].repeat(1, hidden_size), remainds[:, None] * hidden[:, t, :], frame
-        )
-
-    fires = torch.stack(list_fires, 1)
-    frames = torch.stack(list_frames, 1)
-
-    fire_idxs = fires >= threshold
-    frame_fires = torch.zeros_like(hidden)
-    max_label_len = frames[0, fire_idxs[0]].size(0)
-    for b in range(batch_size):
-        frame_fire = frames[b, fire_idxs[b]]
-        frame_len = frame_fire.size(0)
-        frame_fires[b, :frame_len, :] = frame_fire
-
-        if frame_len >= max_label_len:
-            max_label_len = frame_len
-    frame_fires = frame_fires[:, :max_label_len, :]
-    return frame_fires, fires
-
-
-class mae_loss(torch.nn.Module):
-
-    def __init__(self, normalize_length=False):
-        super(mae_loss, self).__init__()
-        self.normalize_length = normalize_length
-        self.criterion = torch.nn.L1Loss(reduction="sum")
-
-    def forward(self, token_length, pre_token_length):
-        loss_token_normalizer = token_length.size(0)
-        if self.normalize_length:
-            loss_token_normalizer = token_length.sum().type(torch.float32)
-        loss = self.criterion(token_length, pre_token_length)
-        loss = loss / loss_token_normalizer
-        return loss
-
-
-def cif(hidden, alphas, threshold):
-    batch_size, len_time, hidden_size = hidden.size()
-
-    # loop varss
-    integrate = torch.zeros([batch_size], device=hidden.device)
-    frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
-    # intermediate vars along time
-    list_fires = []
-    list_frames = []
-
-    for t in range(len_time):
-        alpha = alphas[:, t]
-        distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
-
-        integrate += alpha
-        list_fires.append(integrate)
-
-        fire_place = integrate >= threshold
-        integrate = torch.where(
-            fire_place, integrate - torch.ones([batch_size], device=hidden.device), integrate
-        )
-        cur = torch.where(fire_place, distribution_completion, alpha)
-        remainds = alpha - cur
-
-        frame += cur[:, None] * hidden[:, t, :]
-        list_frames.append(frame)
-        frame = torch.where(
-            fire_place[:, None].repeat(1, hidden_size), remainds[:, None] * hidden[:, t, :], frame
-        )
-
-    fires = torch.stack(list_fires, 1)
-    frames = torch.stack(list_frames, 1)
-    list_ls = []
-    len_labels = torch.round(alphas.sum(-1)).int()
-    max_label_len = len_labels.max()
-    for b in range(batch_size):
-        fire = fires[b, :]
-        l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
-        pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)
-        list_ls.append(torch.cat([l, pad_l], 0))
-    return torch.stack(list_ls, 0), fires
-
-
-def cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=False):
-    batch_size, len_time = alphas.size()
-    device = alphas.device
-    dtype = alphas.dtype
-
-    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
-
-    fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
-
-    if torch.cuda.get_device_name() == "Iluvatar MR-V100":
-        prefix_sum = torch.cumsum(alphas, dim=1)
-    else:
-        prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
-            torch.float32
-        )  # cumsum precision degradation cause wrong result in extreme
-    prefix_sum_floor = torch.floor(prefix_sum)
-    dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
-    dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
-
-    dislocation_prefix_sum_floor[:, 0] = 0
-    dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor
-
-    fire_idxs = dislocation_diff > 0
-    fires[fire_idxs] = 1
-    fires = fires + prefix_sum - prefix_sum_floor
-    if return_fire_idxs:
-        return fires, fire_idxs
-    return fires
-
-
-def cif_v1(hidden, alphas, threshold):
-    fires, fire_idxs = cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=True)
-
-    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).repeat((1, 1, hidden_size)) * hidden, dim=1)
-
-    frames = prefix_sum_hidden[fire_idxs]
-    shift_frames = torch.roll(frames, 1, dims=0)
-
-    batch_len = fire_idxs.sum(1)
-    batch_idxs = torch.cumsum(batch_len, dim=0)
-    shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)
-    shift_batch_idxs[0] = 0
-    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).repeat((1, hidden_size)) * hidden[fire_idxs]
-
-    shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
-    shift_remain_frames[shift_batch_idxs] = 0
-
-    frames = frames - shift_frames + shift_remain_frames - remain_frames
-
-    # max_label_len = batch_len.max()
-    max_label_len = (
-        torch.round(alphas.sum(-1)).int().max()
-    )  # torch.round to calculate the max length
-
-    # 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
-
-
-def cif_wo_hidden(alphas, threshold):
-    batch_size, len_time = alphas.size()
-
-    # loop varss
-    integrate = torch.zeros([batch_size], device=alphas.device)
-    # intermediate vars along time
-    list_fires = []
-
-    for t in range(len_time):
-        alpha = alphas[:, t]
-
-        integrate += alpha
-        list_fires.append(integrate)
-
-        fire_place = integrate >= threshold
-        integrate = torch.where(
-            fire_place,
-            integrate - torch.ones([batch_size], device=alphas.device) * threshold,
-            integrate,
-        )
-
-    fires = torch.stack(list_fires, 1)
-    return fires

test_funasr.py

@@ -1,7 +1,7 @@
 import os
 import time
-import torchaudio
 import torch
+import torchaudio
 from funasr import AutoModel
 from funasr.utils.postprocess_utils import rich_transcription_postprocess
 from utils.calculate import cal_per_cer
@@ -34,18 +34,11 @@ def test_funasr(model_dir, audio_file, answer_file, use_gpu):
     model_name = os.path.basename(model_dir)
     model_type = determine_model_type(model_name)
     
-    if torch.cuda.get_device_name() == "Iluvatar BI-V100" and model_type == "whisper":
-        # 天垓100情况下的Whisper需要绕过不支持算子
-        torch.backends.cuda.enable_flash_sdp(False)
-        torch.backends.cuda.enable_mem_efficient_sdp(False)
-        torch.backends.cuda.enable_math_sdp(True)
-    
     # 不使用VAD, punct，spk模型，就测试原始ASR能力
     model = AutoModel(
         model=model_dir,
         # vad_model="fsmn-vad",
         # vad_kwargs={"max_single_segment_time": 30000},
-        vad_model=None,
         device="cuda:0" if use_gpu else "cpu",
         disable_update=True
     )
@@ -114,14 +107,6 @@ def test_funasr(model_dir, audio_file, answer_file, use_gpu):
                     batch_size_s=300
                 )
                 text = res[0]["text"]
-            # elif model_type == "uni_asr":
-            #     if i == 0:
-            #         os.remove(segment_path)
-            #         continue
-            #     res = model.generate(
-            #         input=segment_path
-            #     )
-            #     text = res[0]["text"]
             else:
                 raise RuntimeError("unknown model type")
             ts2 = time.time()
@@ -142,6 +127,13 @@ def test_funasr(model_dir, audio_file, answer_file, use_gpu):
     return processing_time, acc, generated_text
 
 if __name__ == "__main__":
+    if torch.cuda.is_available():
+        cuda_tensor = torch.randn(2, 2, device='cuda:0')
+        print(f"CUDA device index: {cuda_tensor.get_device()}")
+    else:
+        print("CUDA not available")
+        os._exit(1)
+
     test_result = {
         "time_cuda": 0,
         "acc_cuda": 0,
@@ -176,5 +168,5 @@ if __name__ == "__main__":
         json.dump(test_result, fp, ensure_ascii=False, indent=4)
     # 如果是SUT起来镜像的话，需要加上下面让pod永不停止以迎合k8s deployment, 本地测试以及docker run均不需要
     if K8S_TEST:
-        print(f"Start to sleep indefinitely", flush=True)
+        print(f"Start to sleep indefinity", flush=True)
         time.sleep(100000)