Spaces:
Running
on
T4
Running
on
T4
| # MIT License | |
| # | |
| # Copyright (c) 2022- CNRS | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| import torch | |
| import io | |
| import base64 | |
| import numpy as np | |
| import scipy.io.wavfile | |
| from typing import Text | |
| import streamlit as st | |
| from pyannote.audio import Pipeline | |
| from pyannote.audio import Audio | |
| from pyannote.audio.pipelines.utils.hook import TimingHook | |
| from pyannote.core import Segment | |
| import streamlit.components.v1 as components | |
| def to_base64(waveform: np.ndarray, sample_rate: int = 16000) -> Text: | |
| """Convert waveform to base64 data""" | |
| waveform /= np.max(np.abs(waveform)) + 1e-8 | |
| with io.BytesIO() as content: | |
| scipy.io.wavfile.write(content, sample_rate, waveform) | |
| content.seek(0) | |
| b64 = base64.b64encode(content.read()).decode() | |
| b64 = f"data:audio/x-wav;base64,{b64}" | |
| return b64 | |
| PYANNOTE_LOGO = "https://avatars.githubusercontent.com/u/7559051?s=400&v=4" | |
| EXCERPT = 120 | |
| st.set_page_config(page_title="pyannote pretrained pipelines", page_icon=PYANNOTE_LOGO) | |
| col1, col2 = st.columns([0.2, 0.8], gap="small") | |
| with col1: | |
| st.image(PYANNOTE_LOGO) | |
| with col2: | |
| st.markdown( | |
| """ | |
| # pretrained pipelines | |
| Make the most of [pyannote](https://github.com/pyannote) thanks to our [consulting services](https://herve.niderb.fr/consulting.html) | |
| """ | |
| ) | |
| PIPELINES = [ | |
| "pyannote/speaker-diarization-3.1", | |
| # "pyannote/speaker-diarization-3.0", | |
| ] | |
| audio = Audio(sample_rate=16000, mono=True) | |
| selected_pipeline = st.selectbox("Select a pretrained pipeline", PIPELINES, index=0) | |
| with st.spinner("Loading pipeline..."): | |
| try: | |
| use_auth_token = st.secrets["PYANNOTE_TOKEN"] | |
| except FileNotFoundError: | |
| use_auth_token = None | |
| except KeyError: | |
| use_auth_token = None | |
| pipeline = Pipeline.from_pretrained( | |
| selected_pipeline, use_auth_token=use_auth_token | |
| ) | |
| if torch.cuda.is_available(): | |
| pipeline.to(torch.device("cuda")) | |
| uploaded_file = st.file_uploader("Upload an audio file") | |
| if uploaded_file is not None: | |
| try: | |
| duration = audio.get_duration(uploaded_file) | |
| except RuntimeError as e: | |
| st.error(e) | |
| st.stop() | |
| spinner_message = ( | |
| f"Processing {duration:.0f}s file... " | |
| if duration < EXCERPT | |
| else f"Processing first {EXCERPT:.0f}s of file..." | |
| ) | |
| duration = min(duration, EXCERPT) | |
| waveform, sample_rate = audio.crop(uploaded_file, Segment(0, duration)) | |
| uri = "".join(uploaded_file.name.split()) | |
| file = {"waveform": waveform, "sample_rate": sample_rate, "uri": uri} | |
| with st.spinner(spinner_message): | |
| with TimingHook() as hook: | |
| output = pipeline(file, hook=hook) | |
| processing_time = file["timing"]["total"] | |
| faster_than_real_time = duration / processing_time | |
| st.success( | |
| f"Processed {duration:.0f}s of audio in {processing_time:.1f}s ({faster_than_real_time:.1f}x faster than real-time)", | |
| icon="✅", | |
| ) | |
| with open("assets/template.html") as html, open("assets/style.css") as css: | |
| html_template = html.read() | |
| st.markdown("<style>{}</style>".format(css.read()), unsafe_allow_html=True) | |
| colors = [ | |
| "#ffd70033", | |
| "#00ffff33", | |
| "#ff00ff33", | |
| "#00ff0033", | |
| "#9932cc33", | |
| "#00bfff33", | |
| "#ff7f5033", | |
| "#66cdaa33", | |
| ] | |
| num_colors = len(colors) | |
| label2color = { | |
| label: colors[k % num_colors] for k, label in enumerate(sorted(output.labels())) | |
| } | |
| BASE64 = to_base64(waveform.numpy().T) | |
| REGIONS = "" | |
| for segment, _, label in output.itertracks(yield_label=True): | |
| REGIONS += f"regions.addRegion({{start: {segment.start:g}, end: {segment.end:g}, color: '{label2color[label]}', resize : false, drag : false}});" | |
| html = html_template.replace("BASE64", BASE64).replace("REGIONS", REGIONS) | |
| components.html(html, height=250, scrolling=True) | |
| with io.StringIO() as fp: | |
| output.write_rttm(fp) | |
| content = fp.getvalue() | |
| b64 = base64.b64encode(content.encode()).decode() | |
| href = f'<a download="{output.uri}.rttm" href="data:file/text;base64,{b64}">Download</a> result in RTTM file format or run it locally:' | |
| st.markdown(href, unsafe_allow_html=True) | |
| code = f""" | |
| # load pretrained pipeline | |
| from pyannote.audio import Pipeline | |
| pipeline = Pipeline.from_pretrained("{selected_pipeline}", | |
| use_auth_token=HUGGINGFACE_TOKEN) | |
| # (optional) send pipeline to GPU | |
| import torch | |
| pipeline.to(torch.device("cuda")) | |
| # process audio file | |
| output = pipeline("audio.wav")""" | |
| st.code(code, language="python") | |