File size: 4,970 Bytes
50262ab
ce8a201
 
 
 
7c6ede0
 
 
 
50262ab
ce8a201
7c6ede0
50262ab
ce8a201
7c6ede0
ce8a201
7c6ede0
 
 
 
 
50262ab
 
7c6ede0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ce8a201
7c6ede0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ce8a201
7c6ede0
ce8a201
7c6ede0
 
 
50262ab
7c6ede0
 
ede25a6
50262ab
7c6ede0
 
 
 
 
ce8a201
7c6ede0
ce8a201
7c6ede0
 
 
50262ab
7c6ede0
 
ede25a6
50262ab
7c6ede0
 
 
 
ce8a201
7c6ede0
 
ce8a201
7c6ede0
 
 
50262ab
7c6ede0
 
ede25a6
50262ab
7c6ede0
 
 
 
 
 
 
ce8a201
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
from nemo.collections.asr.models import ASRModel
import yt_dlp as youtube_dl
import os
import tempfile
import torch
import gradio as gr
from pydub import AudioSegment

device = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_NAME="nvidia/parakeet-tdt-1.1b"
YT_LENGTH_LIMIT_S=3600

model = ASRModel.from_pretrained(model_name=MODEL_NAME).to(device)
model.eval()

def get_transcripts(audio_path):
    text = model.transcribe([audio_path])[0][0]
    return text

article = (
    "<p style='text-align: center'>"
    "<a href='https://huggingface.co/nvidia/parakeet-tdt-1.1b' target='_blank'>🎙️ Learn more about Parakeet TDT model</a> | "
    "<a href='https://arxiv.org/abs/2304.06795' target='_blank'>📚 TDT ICML paper</a> | "
    "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑‍💻 Repository</a>"
    "</p>"
)
examples = [
    ["data/conversation.wav"],
    ["data/id10270_5r0dWxy17C8-00001.wav"],
]

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, max_filesize=75.0):
    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)
        audio = AudioSegment.from_file(filepath)
        wav_filepath = os.path.join(tmpdirname, "audio.wav")
        audio.export(wav_filepath, format="wav")

    text = get_transcripts(wav_filepath)
    return html_embed_str, text


demo = gr.Blocks()

mf_transcribe = gr.Interface(
    fn=get_transcripts,
    inputs=[
        gr.Audio(sources="microphone", type="filepath")
    ],
    outputs="text",
    theme="huggingface",
    title="Parakeet TDT 1.1B: Transcribe Audio",
    description=(
        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
        " of arbitrary length. TDT models are 75% more efficient than similar size RNNT model"
    ),
    allow_flagging="never",
)

file_transcribe = gr.Interface(
    fn=get_transcripts,
    inputs=[
        gr.Audio(sources="upload", type="filepath", label="Audio file"),
    ],
    outputs="text",
    theme="huggingface",
    title="Parakeet TDT 1.1B: Transcribe Audio",
    description=(
        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
        " of arbitrary length. TDT models are 75% more efficient than similar size RNNT model"
    ),
    allow_flagging="never",
)

youtube_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
    ],
    outputs=["html", "text"],
    theme="huggingface",
    title="Parakeet TDT 1.1B: Transcribe Audio",
    description=(
        "Transcribe microphone or audio inputs with the click of a button! Demo uses the"
        f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
        " of arbitrary length. TDT models are 75% more efficient than similar size RNNT model"
    ),
    allow_flagging="never",
)

with demo:
    gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])

demo.launch()