Initial commit
Browse files- 135.wav +0 -0
- Dockerfile +54 -0
- README.md +1 -1
- app.py +185 -0
- common_voice_zgh_37837257.mp3 +0 -0
- install_beamsearch_decoders.sh +70 -0
- kenlm.bin +3 -0
- packages.txt +2 -0
- pre-requirements.txt +1 -0
- requirements.txt +7 -0
135.wav
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Dockerfile
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#!/usr/bin/env bash
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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Use this script to install KenLM, OpenSeq2Seq decoder, Flashlight decoder, OpenGRM Ngram tool to contaner
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# How to use? Build it from NeMo root folder:
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# 1. git clone https://github.com/NVIDIA/NeMo.git && cd NeMo
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# 2. DOCKER_BUILDKIT=1 docker build -t nemo:23.03.1 -f ./scripts/installers/Dockerfile.ngramtools .
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FROM nvcr.io/nvidia/nemo:24.01.speech
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WORKDIR /workspace/nemo
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COPY ./install_beamsearch_decoders.sh /workspace/nemo/scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh
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RUN /bin/bash scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh
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RUN --mount=target=/tmp/packages.txt,source=packages.txt apt-get update && xargs -r -a /tmp/packages.txt apt-get install -y && rm -rf /var/lib/apt/lists/*
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RUN --mount=target=/tmp/pre-requirements.txt,source=pre-requirements.txt pip install --no-cache-dir -r /tmp/pre-requirements.txt
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RUN --mount=target=/tmp/requirements.txt,source=requirements.txt pip install --no-cache-dir -r /tmp/requirements.txt
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WORKDIR /code
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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CMD ["python", "app.py"]
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README.md
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---
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-
title: ASR N
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emoji: 👀
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colorFrom: purple
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colorTo: gray
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---
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title: ASR N-gram Language Modeling
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emoji: 👀
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colorFrom: purple
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colorTo: gray
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app.py
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from nemo.collections.asr.models import EncDecCTCModelBPE
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from omegaconf import open_dict
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#import yt_dlp as youtube_dl
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import os
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import tempfile
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import torch
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import gradio as gr
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from pydub import AudioSegment
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import time
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device = "cuda" if torch.cuda.is_available() else "cpu"
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MODEL_NAME="ayymen/stt_zgh_fastconformer_ctc_small"
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YT_LENGTH_LIMIT_S=3600
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model = EncDecCTCModelBPE.from_pretrained(model_name=MODEL_NAME).to(device)
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with open_dict(model.cfg):
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model.cfg.decoding.strategy = "beam"
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model.cfg.decoding.beam.beam_size = 256 # Desired Beam Size
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model.cfg.decoding.beam.beam_alpha = 1.5 # Desired Beam Alpha
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model.cfg.decoding.beam.beam_beta = 1.5 # Desired Beam Beta
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model.cfg.decoding.beam.kenlm_path = "kenlm.bin" # Path to KenLM binary file
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model.change_decoding_strategy(model.cfg.decoding)
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model.eval()
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def get_transcripts(audio_path):
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audio = AudioSegment.from_file(audio_path)
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# check if audio is mono 16kHz
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if audio.channels != 1 or audio.frame_rate != 16000:
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audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
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with tempfile.TemporaryDirectory() as tmpdirname:
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audio_path = os.path.join(tmpdirname, "audio.wav")
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audio.export(audio_path, format="wav")
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text = model.transcribe([audio_path])[0]
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else:
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text = model.transcribe([audio_path])[0]
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return text
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'''
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article = (
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"<p style='text-align: center'>"
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"<a href='https://huggingface.co/nvidia/parakeet-rnnt-1.1b' target='_blank'>🎙️ Learn more about Parakeet model</a> | "
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"<a href='https://arxiv.org/abs/2305.05084' target='_blank'>📚 FastConformer paper</a> | "
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"<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑💻 Repository</a>"
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"</p>"
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)
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'''
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EXAMPLES = [
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["135.wav"],
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["common_voice_zgh_37837257.mp3"]
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]
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"""
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YT_EXAMPLES = [
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["https://www.youtube.com/shorts/CSgTSE50MHY"],
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["https://www.youtube.com/shorts/OxQtqOyAFLE"]
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]
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"""
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def _return_yt_html_embed(yt_url):
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video_id = yt_url.split("?v=")[-1]
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if "youtube.com/shorts/" in video_id:
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video_id = video_id.split("/")[-1]
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HTML_str = (
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f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
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" </center>"
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)
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return HTML_str
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def download_yt_audio(yt_url, filename):
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info_loader = youtube_dl.YoutubeDL()
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try:
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info = info_loader.extract_info(yt_url, download=False)
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except youtube_dl.utils.DownloadError as err:
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raise gr.Error(str(err))
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file_length = info["duration_string"]
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file_h_m_s = file_length.split(":")
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file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
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if len(file_h_m_s) == 1:
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file_h_m_s.insert(0, 0)
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if len(file_h_m_s) == 2:
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file_h_m_s.insert(0, 0)
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file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
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+
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if file_length_s > YT_LENGTH_LIMIT_S:
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yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
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file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
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raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
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ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
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with youtube_dl.YoutubeDL(ydl_opts) as ydl:
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try:
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ydl.download([yt_url])
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except youtube_dl.utils.ExtractorError as err:
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raise gr.Error(str(err))
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def yt_transcribe(yt_url, max_filesize=75.0):
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html_embed_str = _return_yt_html_embed(yt_url)
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with tempfile.TemporaryDirectory() as tmpdirname:
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filepath = os.path.join(tmpdirname, "video.mp4")
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download_yt_audio(yt_url, filepath)
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audio = AudioSegment.from_file(filepath)
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audio = audio.set_channels(1).set_frame_rate(16000) # convert to mono 16kHz
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wav_filepath = os.path.join(tmpdirname, "audio.wav")
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audio.export(wav_filepath, format="wav")
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text = get_transcripts(wav_filepath)
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return html_embed_str, text
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demo = gr.Blocks()
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mf_transcribe = gr.Interface(
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fn=get_transcripts,
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inputs=[
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gr.Audio(sources="microphone", type="filepath")
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],
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outputs="text",
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title="Transcribe Audio",
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description=(
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"Transcribe microphone or audio inputs with the click of a button! Demo uses the"
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f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
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" of arbitrary length."
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),
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allow_flagging="never",
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)
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file_transcribe = gr.Interface(
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fn=get_transcripts,
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inputs=[
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gr.Audio(sources="upload", type="filepath", label="Audio file"),
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],
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outputs="text",
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examples=EXAMPLES,
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title="Transcribe Audio",
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description=(
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146 |
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"Transcribe microphone or audio inputs with the click of a button! Demo uses the"
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147 |
+
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
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148 |
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" of arbitrary length."
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),
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allow_flagging="never",
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)
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"""
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youtube_transcribe = gr.Interface(
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fn=yt_transcribe,
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inputs=[
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gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
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],
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outputs=["html", "text"],
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examples=YT_EXAMPLES,
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title="Transcribe Audio",
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description=(
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163 |
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"Transcribe microphone or audio inputs with the click of a button! Demo uses the"
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164 |
+
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and [NVIDIA NeMo](https://github.com/NVIDIA/NeMo) to transcribe audio files"
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" of arbitrary length."
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),
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allow_flagging="never",
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)
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"""
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170 |
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with demo:
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gr.TabbedInterface(
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[
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mf_transcribe,
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file_transcribe,
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#youtube_transcribe
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],
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[
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"Microphone",
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"Audio file",
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#"Youtube Video"
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182 |
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]
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)
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+
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demo.launch()
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common_voice_zgh_37837257.mp3
ADDED
Binary file (28.1 kB). View file
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install_beamsearch_decoders.sh
ADDED
@@ -0,0 +1,70 @@
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#!/usr/bin/env bash
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2 |
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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#
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4 |
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# Licensed under the Apache License, Version 2.0 (the "License");
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5 |
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# you may not use this file except in compliance with the License.
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6 |
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# You may obtain a copy of the License at
|
7 |
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#
|
8 |
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# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
|
16 |
+
# Use this script to install KenLM, OpenSeq2Seq decoder, Flashlight decoder
|
17 |
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shopt -s expand_aliases
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+
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NEMO_PATH=/workspace/nemo # Path to NeMo folder: /workspace/nemo if you use NeMo/Dockerfile
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+
if [ "$#" -eq 1 ]; then
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NEMO_PATH=$1
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fi
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KENLM_MAX_ORDER=10 # Maximum order of KenLM model, also specified in the setup_os2s_decoders.py
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+
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if [ -d "$NEMO_PATH" ]; then
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echo "The folder '$NEMO_PATH' exists."
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else
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echo "Error: The folder '$NEMO_PATH' does not exist. Specify it as a first command line positional argument!"
|
29 |
+
exit 1
|
30 |
+
fi
|
31 |
+
cd $NEMO_PATH
|
32 |
+
|
33 |
+
if [ $(id -u) -eq 0 ]; then
|
34 |
+
alias aptupdate='apt-get update'
|
35 |
+
alias b2install='./b2'
|
36 |
+
else
|
37 |
+
alias aptupdate='sudo apt-get update'
|
38 |
+
alias b2install='sudo ./b2'
|
39 |
+
fi
|
40 |
+
|
41 |
+
aptupdate && apt-get upgrade -y
|
42 |
+
# apt-get install -y swig liblzma-dev && rm -rf /var/lib/apt/lists/* # liblzma needed for flashlight decoder
|
43 |
+
|
44 |
+
# install Boost package for KenLM
|
45 |
+
wget https://boostorg.jfrog.io/artifactory/main/release/1.80.0/source/boost_1_80_0.tar.bz2 --no-check-certificate && tar --bzip2 -xf $NEMO_PATH/boost_1_80_0.tar.bz2 && cd boost_1_80_0 && ./bootstrap.sh && b2install --layout=tagged link=static,shared threading=multi,single install -j4 && cd .. || echo FAILURE
|
46 |
+
export BOOST_ROOT=$NEMO_PATH/boost_1_80_0
|
47 |
+
|
48 |
+
git clone https://github.com/NVIDIA/OpenSeq2Seq
|
49 |
+
cd OpenSeq2Seq
|
50 |
+
git checkout ctc-decoders
|
51 |
+
cd ..
|
52 |
+
mv OpenSeq2Seq/decoders $NEMO_PATH/
|
53 |
+
rm -rf OpenSeq2Seq
|
54 |
+
cd $NEMO_PATH/decoders
|
55 |
+
cp $NEMO_PATH/scripts/installers/setup_os2s_decoders.py ./setup.py
|
56 |
+
./setup.sh
|
57 |
+
|
58 |
+
# install KenLM
|
59 |
+
cd $NEMO_PATH/decoders/kenlm/build && cmake -DKENLM_MAX_ORDER=$KENLM_MAX_ORDER .. && make -j2
|
60 |
+
cd $NEMO_PATH/decoders/kenlm
|
61 |
+
python setup.py install --max_order=$KENLM_MAX_ORDER
|
62 |
+
export KENLM_LIB=$NEMO_PATH/decoders/kenlm/build/bin
|
63 |
+
export KENLM_ROOT=$NEMO_PATH/decoders/kenlm
|
64 |
+
cd ..
|
65 |
+
|
66 |
+
# install Flashlight
|
67 |
+
# git clone https://github.com/flashlight/text && cd text
|
68 |
+
# python setup.py bdist_wheel
|
69 |
+
# pip install dist/*.whl
|
70 |
+
# cd ..
|
kenlm.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5e3106cf41031192efb8bd1c615f2f40fc12b9d9ae7132b5b6e16b50aa8e4b83
|
3 |
+
size 69178189
|
packages.txt
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
ffmpeg
|
2 |
+
libsndfile1
|
pre-requirements.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
Cython
|
requirements.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Cython
|
2 |
+
huggingface-hub==0.23.2
|
3 |
+
# nemo-toolkit[asr]==2.0.0rc1
|
4 |
+
# numpy<2.0.0
|
5 |
+
# ipython
|
6 |
+
# yt_dlp
|
7 |
+
gradio
|