File size: 7,241 Bytes
6c226f9
8596cb6
6c226f9
8596cb6
6c226f9
8596cb6
 
 
 
6c226f9
c7d9204
408571e
 
 
 
 
6c226f9
 
 
 
46e56df
6c226f9
 
 
46e56df
 
 
 
 
 
 
 
 
7d34dd0
46e56df
 
 
 
 
 
 
 
 
 
 
 
 
6c226f9
 
 
 
 
 
 
 
 
ed7ad97
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3b293ec
6c226f9
 
ed7ad97
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3b293ec
61fd23f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6c226f9
 
 
 
 
46e56df
3c0cd8e
 
 
 
 
46e56df
3c0cd8e
46e56df
7d34dd0
 
6c226f9
 
 
 
 
7097513
53e44d0
0566672
 
 
7097513
6c226f9
 
 
46e56df
6c226f9
7d34dd0
 
 
6c226f9
 
 
 
46e56df
 
 
 
 
 
 
6c226f9
46e56df
 
 
 
8596cb6
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
import torch

import gradio as gr
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read

import tempfile
import os

MODEL_NAME = "openai/whisper-large-v3"
BATCH_SIZE = 8
FILE_LIMIT_MB = 100000
YT_LENGTH_LIMIT_S = 360000  # limit to 1 hour YouTube files


device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

all_special_ids = pipe.tokenizer.all_special_ids
transcribe_token_id = all_special_ids[-5]
translate_token_id = all_special_ids[-6]

def transcribe(microphone, file_upload, task):
    warn_output = ""
    if (microphone is not None) and (file_upload is not None):
        warn_output = (
            "警告:您已经上传了一个音频文件并使用了麦克录制。 "
            "录制文件将被使用上传的音频将被丢弃。"
        )
    elif (microphone is None) and (file_upload is None):
        return "错误: 您必须使用麦克风录制或上传音频文件"

    file = microphone if microphone is not None else file_upload
    pipe.model.config.forced_decoder_ids = [
        [2, transcribe_token_id if task == "transcribe" else translate_token_id]
    ]
    # text = pipe(file, return_timestamps=True)["text"]
    text = pipe(file, return_timestamps=True)
    # trans to SRT
    text = convert_to_srt(text)
    return warn_output + text

def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    return HTML_str

def download_yt_audio(yt_url, filename):
    info_loader = youtube_dl.YoutubeDL()
    
    try:
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        raise gr.Error(str(err))
    
    file_length = info["duration_string"]
    file_h_m_s = file_length.split(":")
    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
    
    if len(file_h_m_s) == 1:
        file_h_m_s.insert(0, 0)
    if len(file_h_m_s) == 2:
        file_h_m_s.insert(0, 0)
    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
    
    if file_length_s > YT_LENGTH_LIMIT_S:
        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
    
    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
    
    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            raise gr.Error(str(err))


def yt_transcribe(yt_url, task):
    html_embed_str = _return_yt_html_embed(yt_url)

    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        download_yt_audio(yt_url, filepath)
        with open(filepath, "rb") as f:
            inputs = f.read()

    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}

    
    text = pipe(inputs,return_timestamps=True)    
    # text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"language":"zh"}, return_timestamps=True)
    # text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
    # text = pipe("audio.mp3",return_timestamps=True)
    #trans to SRT
    text= convert_to_srt(text)
    return html_embed_str, text

# SRT prepare
# Assuming srt format is a sequence of subtitles with index, time range and text
def convert_to_srt(input):
  output = ""
  index = 1
  for chunk in input["chunks"]:
    start, end = chunk["timestamp"]
    text = chunk["text"]
    if end is None:
      end = "None"
    # Convert seconds to hours:minutes:seconds,milliseconds format
    start = format_time(start)
    end = format_time(end)
    output += f"{index}\n{start} --> {end}\n{text}\n\n"
    index += 1
  return output

# Helper function to format time
def format_time(seconds):
  if seconds == "None":
    return seconds
  hours = int(seconds // 3600)
  minutes = int((seconds % 3600) // 60)
  seconds = int(seconds % 60)
  milliseconds = int((seconds % 1) * 1000)
  return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
demo = gr.Blocks()
mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
        gr.inputs.Audio(source="upload", type="filepath", optional=True),
        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
    ],
    outputs="text",
    layout="horizontal",
    theme="huggingface",
    title="Audio-to-Text-SRT 自动生成字幕",
    description=(
        "直接在网页录音或上传音频文件,加入Youtube连接,轻松转换为文字和字幕格式! 本演示采用"
        f" 模型 [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) 和 🤗 Transformers 转换任意长度的"
        "音视频文件!使用GPU转换效率会大幅提高,大约每小时 $0.6 约相当于人民币 5 元。 如果您有较长内容,需要更快的转换速度,请私信作者微信 1259388,并备注“语音转文字”"
    ),
    allow_flagging="never",
)
yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        # gr.inputs.Radio(["转译", "翻译"], label="Task", default="transcribe")
        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),

    ],
    outputs=["html", "text"],
    layout="horizontal",
    theme="huggingface",
    title="Audio-to-Text-SRT 自动生成字幕",
    description=(
      "直接在网页录音或上传音频文件,加入Youtube连接,轻松转换为文字和字幕格式! 本演示采用"
        f" 模型 [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) 和 🤗 Transformers 转换任意长度的"
        "音视频文件!使用GPU转换效率会大幅提高,大约每小时 $0.6 约相当于人民币 5 元。 如果您有较长内容,需要更快的转换速度,请私信作者微信 1259388,并备注“语音转文字”"
    ),
    allow_flagging="never",
)

# # Load the images
# image1 = Image("wechatqrcode.jpg")
# image2 = Image("paypalqrcode.png")

# # Define a function that returns the images and captions
# def display_images():
#     return image1, "WeChat Pay", image2, "PayPal"

with demo:
    gr.TabbedInterface([mf_transcribe, yt_transcribe], ["转译音频成文字", "YouTube转字幕"])
# Create a gradio interface with no inputs and four outputs
# gr.Interface(display_images, [], [gr.outputs.Image(), gr.outputs.Textbox(), gr.outputs.Image(), gr.outputs.Textbox()], layout="horizontal").launch()
demo.launch(enable_queue=True)