feat: update to latest pyannote and wavesurfer (#3)

app.py

@@ -23,10 +23,10 @@
 
 import io
 import base64
+import torch
 import numpy as np
 import scipy.io.wavfile
 from typing import Text
-from huggingface_hub import HfApi
 import streamlit as st
 from pyannote.audio import Pipeline
 from pyannote.audio import Audio
@@ -49,32 +49,47 @@ def to_base64(waveform: np.ndarray, sample_rate: int = 16000) -> Text:
 PYANNOTE_LOGO = "https://avatars.githubusercontent.com/u/7559051?s=400&v=4"
 EXCERPT = 30.0
 
-st.set_page_config(
-    page_title="pyannote.audio pretrained pipelines", page_icon=PYANNOTE_LOGO
-)
+st.set_page_config(page_title="pyannote pretrained pipelines", page_icon=PYANNOTE_LOGO)
 
+col1, col2 = st.columns([0.2, 0.8], gap="small")
 
-st.sidebar.image(PYANNOTE_LOGO)
+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)
+"""
+    )
 
-st.markdown("""# 🎹 Pretrained pipelines
-""")
 
 PIPELINES = [
-    p.modelId
-    for p in HfApi().list_models(filter="pyannote-audio-pipeline")
-    if p.modelId.startswith("pyannote/")
+    "pyannote/speaker-diarization-3.0",
 ]
 
 audio = Audio(sample_rate=16000, mono=True)
 
-selected_pipeline = st.selectbox("Select a pipeline", PIPELINES, index=0)
+selected_pipeline = st.selectbox("Select a pretrained pipeline", PIPELINES, index=0)
+
 
 with st.spinner("Loading pipeline..."):
-    pipeline = Pipeline.from_pretrained(selected_pipeline, use_auth_token=st.secrets["PYANNOTE_TOKEN"])
+    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("Choose an audio file")
+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:
@@ -86,12 +101,12 @@ def to_base64(waveform: np.ndarray, sample_rate: int = 16000) -> Text:
     uri = "".join(uploaded_file.name.split())
     file = {"waveform": waveform, "sample_rate": sample_rate, "uri": uri}
 
-    with st.spinner(f"Processing first {EXCERPT:g} seconds..."):
+    with st.spinner(f"Processing {EXCERPT:g} seconds..."):
         output = pipeline(file)
 
-    with open('assets/template.html') as html, open('assets/style.css') as css:
+    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)
+        st.markdown("<style>{}</style>".format(css.read()), unsafe_allow_html=True)
 
     colors = [
         "#ffd70033",
@@ -105,50 +120,36 @@ def to_base64(waveform: np.ndarray, sample_rate: int = 16000) -> Text:
     ]
     num_colors = len(colors)
 
-    label2color = {label: colors[k % num_colors] for k, label in enumerate(sorted(output.labels()))}
+    label2color = {
+        label: colors[k % num_colors] for k, label in enumerate(sorted(output.labels()))
+    }
 
     BASE64 = to_base64(waveform.numpy().T)
 
     REGIONS = ""
-    LEGENDS = ""
-    labels=[]
     for segment, _, label in output.itertracks(yield_label=True):
-        REGIONS += f"var re = wavesurfer.addRegion({{start: {segment.start:g}, end: {segment.end:g}, color: '{label2color[label]}', resize : false, drag : false}});"
-        if not label in labels:
-            LEGENDS += f"<li><span style='background-color:{label2color[label]}'></span>{label}</li>"
-            labels.append(label)
+        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)
-    st.markdown("<div style='overflow : auto'><ul class='legend'>"+LEGENDS+"</ul></div>", unsafe_allow_html=True)
-
-    st.markdown("---")
 
     with io.StringIO() as fp:
         output.write_rttm(fp)
         content = fp.getvalue()
-
         b64 = base64.b64encode(content.encode()).decode()
-        href = f'Download as <a download="{output.uri}.rttm" href="data:file/text;base64,{b64}">RTTM</a> or run it on the whole {int(duration):d}s file:'
+        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"""
-    from pyannote.audio import Pipeline
-    pipeline = Pipeline.from_pretrained("{selected_pipeline}")
-    output = pipeline("{uploaded_file.name}")
-    """
-    st.code(code, language='python')
-
-
-
-st.sidebar.markdown(
-    """
--------------------
-
-To use these pipelines on more and longer files on your own (GPU, hence much faster) servers, check the [documentation](https://github.com/pyannote/pyannote-audio).  
+# load pretrained pipeline
+from pyannote.audio import Pipeline
+pipeline = Pipeline.from_pretrained("{selected_pipeline}", 
+                                    use_auth_token=HUGGINGFACE_TOKEN)
 
-For [technical questions](https://github.com/pyannote/pyannote-audio/discussions) and [bug reports](https://github.com/pyannote/pyannote-audio/issues), please check [pyannote.audio](https://github.com/pyannote/pyannote-audio) Github repository.
+# (optional) send pipeline to GPU
+import torch
+pipeline.to(torch.device("cuda"))
 
-For commercial enquiries and scientific consulting, please contact [me](mailto:[email protected]).
-"""
-)
+# process audio file
+output = pipeline("audio.wav")"""
+    st.code(code, language="python")

assets/template.html

@@ -1,46 +1,41 @@
-<script src="https://unpkg.com/wavesurfer.js"></script>
-<script src="https://unpkg.com/wavesurfer.js/dist/plugin/wavesurfer.regions.min.js"></script>
-<script src="https://unpkg.com/wavesurfer.js/dist/plugin/wavesurfer.timeline.min.js"></script>
-<br>
-<div id="waveform"></div>
-<div id="timeline"></div>
-<br>
-<div><button onclick="play()" id="ppb">Play</button><div>
-<script type="text/javascript">
-	var labels=[];
-	var wavesurfer = WaveSurfer.create({
-		container: '#waveform',
-		barGap: 2,
-		barHeight: 3,		
- 		barWidth: 3,
-		barRadius: 2,
-		plugins: [
-			WaveSurfer.regions.create({}),
-			WaveSurfer.timeline.create({
-				container: "#timeline",
-				notchPercentHeight: 40,
-				primaryColor: "#444",
-				primaryFontColor: "#444"
-                        })
-		]
-	});
-	wavesurfer.load('BASE64');
-	wavesurfer.on('ready', function () {
-		wavesurfer.play(); 
-	});
-	wavesurfer.on('play',function() {
-		document.getElementById('ppb').innerHTML = "Pause";
-	});
-	wavesurfer.on('pause',function() {
-		document.getElementById('ppb').innerHTML = "Play";
-	});
+<script type="module">
+import WaveSurfer from 'https://unpkg.com/wavesurfer.js@7/dist/wavesurfer.esm.js'
+import RegionsPlugin from 'https://unpkg.com/wavesurfer.js@7/dist/plugins/regions.esm.js'
+
+
+var labels=[];
+const wavesurfer = WaveSurfer.create({
+	container: '#waveform',
+	barGap: 2,
+	barHeight: 3,		
+ 	barWidth: 3,
+	barRadius: 2,
+});
+
+const regions = wavesurfer.registerPlugin(RegionsPlugin.create())
+
+wavesurfer.load('BASE64');
+wavesurfer.on('ready', function () {
+	wavesurfer.play(); 
+});
+
+wavesurfer.on('decode', function () {
+
 	REGIONS
-	document.addEventListener('keyup', event => {
-		if (event.code === 'Space') {
-			play();
-		}
-	})
-	function play(){
-		wavesurfer.isPlaying() ? wavesurfer.pause() : wavesurfer.play();
-	}
+
+	wavesurfer.play();
+
+});
+
+wavesurfer.on('click', () => {
+	play();
+});
+
+
+function play(){
+	wavesurfer.isPlaying() ? wavesurfer.pause() : wavesurfer.play();
+}
+
 </script>
+<div id="waveform"></div>
+

requirements.txt

@@ -1,7 +1 @@
-torch==1.11.0 
-torchvision==0.12.0 
-torchaudio==0.11.0 
-torchtext==0.12.0
-speechbrain==0.5.12
-pyannote-audio>=2.1
-
+pyannote-audio==3.0.1