update timestamp and add ploter

shixian.shi

2023-03-03 d6c5d9394a088062c834f4d35d94d46c980ea6e0

update timestamp and add ploter

 funasr/runtime/python/onnxruntime/demo.py

@@ -6,7 +6,7 @@

model = Paraformer(model_dir, batch_size=1)

wav_path = ['/Users/shixian/code/funasr2/export/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/example/asr_example.wav']
wav_path = ['/Users/shixian/code/funasr2/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav']

result = model(wav_path)
print(result)

 funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py

@@ -1,6 +1,7 @@
# -*- encoding: utf-8 -*-
# @Author: SWHL
# @Contact: liekkaskono@163.com
from cgitb import text
import os.path
from pathlib import Path
from typing import List, Union, Tuple
@@ -23,6 +24,7 @@
    def __init__(self, model_dir: Union[str, Path] = None,
                 batch_size: int = 1,
                 device_id: Union[str, int] = "-1",
                 plot_timestamp: bool = True,
                 ):

        if not Path(model_dir).exists():
@@ -41,7 +43,7 @@
        )
        self.ort_infer = OrtInferSession(model_file, device_id)
        self.batch_size = batch_size
        self.plot = True
        self.plot = plot_timestamp

    def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
        waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
@@ -76,7 +78,26 @@

    def plot_wave_timestamp(self, wav, text_timestamp):
        # TODO: Plot the wav and timestamp results with matplotlib
        import pdb; pdb.set_trace()
        import matplotlib
        matplotlib.use('Agg')
        matplotlib.rc("font", family='Alibaba PuHuiTi')  # set it to a font that your system supports
        import matplotlib.pyplot as plt
        fig, ax1 = plt.subplots(figsize=(11, 3.5), dpi=320)
        ax2 = ax1.twinx()
        ax2.set_ylim([0, 2.0])
        # plot waveform
        ax1.set_ylim([-0.3, 0.3])
        time = np.arange(wav.shape[0]) / 16000
        ax1.plot(time, wav/wav.max()*0.3, color='gray', alpha=0.4)
        # plot lines and text
        for (char, start, end) in text_timestamp:
            ax1.vlines(start, -0.3, 0.3, ls='--')
            ax1.vlines(end, -0.3, 0.3, ls='--')
            x_adj = 0.045 if char != '<sil>' else 0.12
            ax1.text((start + end) * 0.5 - x_adj, 0, char)
        # plt.legend()
        plotname = "debug.png"
        plt.savefig(plotname, bbox_inches='tight')

    def load_data(self,
                  wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:

 funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py

@@ -1,11 +1,11 @@
import numpy as np


def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0):
def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1.5):
    if not len(char_list):
        return []
    START_END_THRESHOLD = 5
    MAX_TOKEN_DURATION = 14
    MAX_TOKEN_DURATION = 30
    TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
    cif_peak = us_cif_peak.reshape(-1)
    num_frames = cif_peak.shape[-1]
@@ -16,7 +16,7 @@
    new_char_list = []
    # for bicif model trained with large data, cif2 actually fires when a character starts
    # so treat the frames between two peaks as the duration of the former token
    fire_place = np.where(cif_peak>1.0-1e-4)[0] - 1.5  # np format
    fire_place = np.where(cif_peak>1.0-1e-4)[0] + total_offset  # np format
    num_peak = len(fire_place)
    assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
    # begin silence
@@ -27,7 +27,7 @@
    # tokens timestamp
    for i in range(len(fire_place)-1):
        new_char_list.append(char_list[i])
        if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
        if i == len(fire_place)-2 or MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
            timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE])
        else:
            # cut the duration to token and sil of the 0-weight frames last long
@@ -55,4 +55,5 @@
    for char, timestamp in zip(new_char_list, timestamp_list):
        if char != '<sil>':
            res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
    return res, res_total
    return res, res_total