Create modelling processes

verbanote/loaders.py

@@ -1,5 +1,6 @@
 import locale
 from pathlib import Path
+import subprocess
 from whisper import Whisper
 from pyannote.audio import Pipeline
 import torch
@@ -12,17 +13,37 @@ def prep() -> None:
     # download and add ffmpeg to env
     static_ffmpeg.add_paths()
 
-def audiofile(drive_url: str, path: str) -> Path | None:
+
+def audiofile(drive_url: str, path: Path) -> Path | None:
     if not drive_url:
         return None
-    fn = Path.joinpath(Path(path), "interview")
-    gdown.download(drive_url, str(fn))
+    gdown.download(drive_url, "infile")
+    fn = Path.joinpath(path, "interview.wav")
+    subprocess.run(
+        [
+            "ffmpeg",
+            "-i",
+            "{repr(video_path)}",
+            "-vn",
+            "-acodec",
+            "pcm_s16le",
+            "-ar",
+            "16000",
+            "-ac",
+            "1",
+            "-y",
+            fn,
+        ]
+    )
     return fn
 
+
 def diarization(access_token: str | None) -> Pipeline:
-    return Pipeline.from_pretrained(
+    pipeline = Pipeline.from_pretrained(
         "pyannote/speaker-diarization", use_auth_token=access_token
     )
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    return pipeline.to(device)
 
 
 def whisper() -> Whisper:

verbanote/process.py

@@ -0,0 +1,110 @@
+import os
+import re
+import json
+from pathlib import Path
+from pyannote.audio import Pipeline
+from pydub import AudioSegment
+from whisper import Whisper
+
+MILLISECONDS_TO_SPACE = 2000
+
+
+def diarize(audiofile: Path, pipeline: Pipeline, output_path: Path) -> Path:
+    audiofile_prepended = _add_audio_silence(audiofile)
+
+    DEMO_FILE = {"uri": "blabla", "audio": audiofile_prepended}
+    dz = pipeline(DEMO_FILE)
+
+    out_file = Path.joinpath(output_path, "diarization.txt")
+    with open(out_file, "w") as text_file:
+        text_file.write(str(dz))
+
+    print("Diarized:")
+    print(*list(dz.itertracks(yield_label=True))[:10], sep="\n")
+
+    return out_file
+
+
+def transcribe(
+    model: Whisper,
+    diarized_groups: list,
+    output_path: Path,
+    lang: str = "en",
+    word_timestamps: bool = True,
+):
+    for i in range(len(diarized_groups)):
+        f = {Path.joinpath(output_path, str(i))}
+        audio_f = f"{f}.wav"
+        json_f = f"{f}.json"
+        result = model.transcribe(
+            audio=audio_f, language=lang, word_timestamps=word_timestamps
+        )
+        with open(json_f, "w") as outfile:
+            json.dump(result, outfile, indent=4)
+
+
+def save_diarized_audio_files(
+    diarization: Path, audiofile: Path, output_path: Path
+) -> list:
+    groups = _group_speakers(diarization)
+    _save_individual_audio_files(audiofile, groups, output_path)
+    return groups
+
+
+def _add_audio_silence(audiofile) -> Path:
+    spacermilli = MILLISECONDS_TO_SPACE
+    spacer = AudioSegment.silent(duration=spacermilli)
+    audio = AudioSegment.from_wav(audiofile)
+    audio = spacer.append(audio, crossfade=0)
+    out_file = Path.joinpath(Path(os.path.dirname(audiofile)), "interview_prepend.wav")
+    audio.export(out_file, format="wav")
+
+    return out_file
+
+
+def _save_individual_audio_files(
+    audiofile: Path, groups: list[str], output_path: Path
+) -> None:
+    audio = AudioSegment.from_wav(audiofile)
+    gidx = -1
+    for g in groups:
+        start = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=g[0])[0]
+        end = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=g[-1])[1]
+        start = _millisec(start)  # - spacermilli
+        end = _millisec(end)  # - spacermilli
+        gidx += 1
+        audio[start:end].export(
+            f"{Path.joinpath(output_path, str(gidx))}.wav", format="wav"
+        )
+
+
+def _group_speakers(diarization_file: Path) -> list:
+    dzs = open(diarization_file).read().splitlines()
+
+    groups: list = []
+    g = []
+    lastend = 0
+
+    for d in dzs:
+        if g and (g[0].split()[-1] != d.split()[-1]):  # same speaker
+            groups.append(g)
+            g = []
+
+        g.append(d)
+
+        end = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=d)[1]
+        end = _millisec(end)
+        if lastend > end:  # segment engulfed by a previous segment
+            groups.append(g)
+            g = []
+        else:
+            lastend = end
+    if g:
+        groups.append(g)
+    return groups
+
+
+def _millisec(timeStr):
+    spl = timeStr.split(":")
+    s = (int)((int(spl[0]) * 60 * 60 + int(spl[1]) * 60 + float(spl[2])) * 1000)
+    return s

verbanote/rp_handler.py

@@ -2,12 +2,15 @@ from pathlib import Path
 import runpod
 from runpod.serverless import os
 import loaders
+import process
+
+
+output_path = os.environ.get("VERBANOTE_OUTPUT_PATH", "/transcriptions")
+output_path = Path(output_path)
+input_path = os.environ.get("VERBANOTE_INPUT_PATH", "/audiofiles")
+input_path = Path(input_path)
 
 access_token = os.environ.get("VERBANOTE_HF_TOKEN")
-output_path = os.environ.get("VERBANOTE_OUTPUT_PATH", "/transcriptions")
-output_path = str(Path(output_path))
-input_path = os.environ.get("VERBANOTE_INPUT_PATH", "/audiofiles")
-input_path = str(Path(input_path))
 
 loaders.prep()
 diarize_pipeline = loaders.diarization(access_token)
@@ -20,12 +23,32 @@ def handler(job):
     if not audiofile:
         return {"error": "missing audio file location"}
 
+    diarized = process.diarize(audiofile, diarize_pipeline, output_path)
+    diarized_groups = process.save_diarized_audio_files(
+        diarized, audiofile, output_path
+    )
+    process.transcribe(
+        model=whisper_model, diarized_groups=diarized_groups, output_path=output_path
+    )
+
     return {
         "speaker_timings": "s3-address-to-speakers",
         "transcription_text": "s3-address-to-transcription",
         "transcription_page": "web-address-to-deployment",
     }
 
+# speakers = {
+#     # speaker, textboxcolor, speaker color
+#     "SPEAKER_00": ("SPEAKER00", "white", "darkgreen"),
+#     "SPEAKER_01": ("SPEAKER01", "white", "darkorange"),
+#     "SPEAKER_02": ("SPEAKER02", "white", "darkred"),
+#     "SPEAKER_03": ("SPEAKER03", "white", "darkblue"),
+#     "SPEAKER_04": ("SPEAKER04", "white", "darkyellow"),
+#     "SPEAKER_05": ("SPEAKER05", "white", "lightgreen"),
+#     "SPEAKER_06": ("SPEAKER06", "white", "lightred"),
+#     "SPEAKER_07": ("SPEAKER07", "white", "lightblue"),
+# }
+
 
 if __name__ == "__main__":
     runpod.serverless.start({"handler": handler})