File size: 6,086 Bytes
143d264 |
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 |
from nemo.collections.asr.models import EncDecCTCModelBPE
from omegaconf import open_dict
#import yt_dlp as youtube_dl
import os
import tempfile
import torch
import gradio as gr
from pydub import AudioSegment
import time
device = "cuda" if torch.cuda.is_available() else "cpu"
MODEL_NAME="ayymen/stt_zgh_fastconformer_ctc_small"
YT_LENGTH_LIMIT_S=3600
model = EncDecCTCModelBPE.from_pretrained(model_name=MODEL_NAME).to(device)
with open_dict(model.cfg):
model.cfg.decoding.strategy = "beam"
model.cfg.decoding.beam.beam_size = 256 # Desired Beam Size
model.cfg.decoding.beam.beam_alpha = 1.5 # Desired Beam Alpha
model.cfg.decoding.beam.beam_beta = 1.5 # Desired Beam Beta
model.cfg.decoding.beam.kenlm_path = "kenlm.bin" # Path to KenLM binary file
model.change_decoding_strategy(model.cfg.decoding)
model.eval()
def get_transcripts(audio_path):
audio = AudioSegment.from_file(audio_path)
# check if audio is mono 16kHz
if audio.channels != 1 or audio.frame_rate != 16000:
audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
with tempfile.TemporaryDirectory() as tmpdirname:
audio_path = os.path.join(tmpdirname, "audio.wav")
audio.export(audio_path, format="wav")
text = model.transcribe([audio_path])[0]
else:
text = model.transcribe([audio_path])[0]
return text
'''
article = (
"<p style='text-align: center'>"
"<a href='https://huggingface.co/nvidia/parakeet-rnnt-1.1b' target='_blank'>🎙️ Learn more about Parakeet model</a> | "
"<a href='https://arxiv.org/abs/2305.05084' target='_blank'>📚 FastConformer paper</a> | "
"<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑💻 Repository</a>"
"</p>"
)
'''
EXAMPLES = [
["135.wav"],
["common_voice_zgh_37837257.mp3"]
]
"""
YT_EXAMPLES = [
["https://www.youtube.com/shorts/CSgTSE50MHY"],
["https://www.youtube.com/shorts/OxQtqOyAFLE"]
]
"""
def _return_yt_html_embed(yt_url):
video_id = yt_url.split("?v=")[-1]
if "youtube.com/shorts/" in video_id:
video_id = video_id.split("/")[-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)
audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
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",
title="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."
),
allow_flagging="never",
)
file_transcribe = gr.Interface(
fn=get_transcripts,
inputs=[
gr.Audio(sources="upload", type="filepath", label="Audio file"),
],
outputs="text",
examples=EXAMPLES,
title="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."
),
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"],
examples=YT_EXAMPLES,
title="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."
),
allow_flagging="never",
)
"""
with demo:
gr.TabbedInterface(
[
mf_transcribe,
file_transcribe,
#youtube_transcribe
],
[
"Microphone",
"Audio file",
#"Youtube Video"
]
)
demo.launch()
|