Spaces:
Runtime error
Runtime error
File size: 4,975 Bytes
8aaccc9 96b8638 8d18e6b 8aaccc9 96b8638 8aaccc9 4765e0e 8aaccc9 dbe8f3a 8aaccc9 dbe8f3a 8aaccc9 4765e0e 8aaccc9 dbe8f3a 8aaccc9 dbe8f3a 8aaccc9 2ddcddc 8aaccc9 2ddcddc 8aaccc9 |
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 |
import logging
import warnings
import gradio as gr
import pytube as pt
import torch
from huggingface_hub import model_info
from transformers import pipeline
from transformers.utils.logging import disable_progress_bar
warnings.filterwarnings("ignore")
disable_progress_bar()
DEFAULT_MODEL_NAME = "bofenghuang/whisper-large-v2-cv11-german"
# make sure no OOM
MODEL_NAMES = [
"bofenghuang/whisper-medium-cv11-german",
"bofenghuang/whisper-large-v2-cv11-german",
]
LANG = "de"
CHUNK_LENGTH_S = 30
MAX_NEW_TOKENS = 225
logging.basicConfig(
format="%(asctime)s [%(levelname)s] [%(name)s] %(message)s",
datefmt="%Y-%m-%dT%H:%M:%SZ",
)
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
device = 0 if torch.cuda.is_available() else "cpu"
logger.info(f"Model will be loaded on device `{device}`")
cached_models = {}
def print_cuda_memory_info():
used_mem, tot_mem = torch.cuda.mem_get_info()
logger.info(f"CUDA memory info - Free: {used_mem / 1024 ** 3:.2f} Gb, used: {(tot_mem - used_mem) / 1024 ** 3:.2f} Gb, total: {tot_mem / 1024 ** 3:.2f} Gb")
def print_memory_info():
# todo
if device == "cpu":
pass
else:
print_cuda_memory_info()
def maybe_load_cached_pipeline(model_name):
pipe = cached_models.get(model_name)
if pipe is None:
# load pipeline
# todo: set decoding option for pipeline
pipe = pipeline(
task="automatic-speech-recognition",
model=model_name,
chunk_length_s=CHUNK_LENGTH_S,
device=device,
)
# set forced_decoder_ids
pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=LANG, task="transcribe")
# limit genneration max length
pipe.model.config.max_length = MAX_NEW_TOKENS + 1
logger.info(f"`{model_name}` pipeline has been initialized")
print_memory_info()
cached_models[model_name] = pipe
return pipe
def transcribe(microphone, file_upload, model_name):
warn_output = ""
if (microphone is not None) and (file_upload is not None):
warn_output = (
"WARNING: You've uploaded an audio file and used the microphone. "
"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
)
elif (microphone is None) and (file_upload is None):
return "ERROR: You have to either use the microphone or upload an audio file"
file = microphone if microphone is not None else file_upload
pipe = maybe_load_cached_pipeline(model_name)
text = pipe(file)["text"]
logger.info(f"Transcription by `{model_name}`: {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 yt_transcribe(yt_url, model_name):
yt = pt.YouTube(yt_url)
html_embed_str = _return_yt_html_embed(yt_url)
stream = yt.streams.filter(only_audio=True)[0]
stream.download(filename="audio.mp3")
pipe = maybe_load_cached_pipeline(model_name)
text = pipe("audio.mp3")["text"]
logger.info(f"Transcription: {text}")
return html_embed_str, text
# load default model
maybe_load_cached_pipeline(DEFAULT_MODEL_NAME)
demo = gr.Blocks()
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Record"),
gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Upload File"),
gr.inputs.Dropdown(choices=MODEL_NAMES, default=DEFAULT_MODEL_NAME, label="Whisper Model"),
],
# outputs="text",
outputs=gr.outputs.Textbox(label="Transcription"),
layout="horizontal",
theme="huggingface",
title="Whisper German Demo 🇩🇪 : Transcribe Audio",
description="Transcribe long-form microphone or audio inputs with the click of a button!",
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.Dropdown(choices=MODEL_NAMES, default=DEFAULT_MODEL_NAME, label="Whisper Model"),
],
# outputs=["html", "text"],
outputs=[
gr.outputs.HTML(label="YouTube Page"),
gr.outputs.Textbox(label="Transcription"),
],
layout="horizontal",
theme="huggingface",
title="Whisper German Demo 🇩🇪 : Transcribe YouTube",
description="Transcribe long-form YouTube videos with the click of a button!",
allow_flagging="never",
)
with demo:
gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
# demo.launch(server_name="0.0.0.0", debug=True, share=True)
demo.launch(enable_queue=True)
|