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from nemo.collections.asr.models import EncDecRNNTBPEModel
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-rnnt-1.1b"
YT_LENGTH_LIMIT_S=3600
model = EncDecRNNTBPEModel.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-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 = [
["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 RNNT 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."
),
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 RNNT 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."
),
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 RNNT 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."
),
allow_flagging="never",
)
with demo:
gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"])
demo.launch()
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