ComeOnOliver/skillshub

Whisper

Overview

Transcribe audio with OpenAI's Whisper — the state-of-the-art speech recognition model. This skill covers local Whisper (Python), faster-whisper (CTranslate2, 4x faster), whisper.cpp (CPU-optimized C++), and the OpenAI Whisper API. Includes subtitle generation (SRT/VTT/JSON), multi-language transcription, translation to English, speaker diarization, word-level timestamps, and production pipeline patterns for podcasts, meetings, and video subtitles.

Instructions

Step 1: Choose Your Runtime

Option A — OpenAI Whisper (original Python):

pip install openai-whisper
# Models: tiny (39M), base (74M), small (244M), medium (769M), large-v3 (1.5G)

Option B — faster-whisper (recommended for local, 4x faster):

pip install faster-whisper
# Uses CTranslate2 — INT8 quantization, runs well on CPU

Option C — whisper.cpp (best for CPU, minimal dependencies):

git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp && make
# Download model
bash models/download-ggml-model.sh base.en

Option D — OpenAI API (no local GPU needed):

pip install openai
export OPENAI_API_KEY="sk-..."

Step 2: Basic Transcription

faster-whisper (recommended):

from faster_whisper import WhisperModel

model = WhisperModel("base", device="cpu", compute_type="int8")
# GPU: model = WhisperModel("large-v3", device="cuda", compute_type="float16")

segments, info = model.transcribe("episode.mp3", beam_size=5)
print(f"Language: {info.language} (prob: {info.language_probability:.2f})")

for segment in segments:
    print(f"[{segment.start:.2f}s → {segment.end:.2f}s] {segment.text}")

OpenAI Whisper (original):

import whisper

model = whisper.load_model("base")  # tiny, base, small, medium, large-v3
result = model.transcribe("episode.mp3")

print(result["text"])
for segment in result["segments"]:
    print(f"[{segment['start']:.1f}s - {segment['end']:.1f}s] {segment['text']}")

whisper.cpp (CLI):

./main -m models/ggml-base.en.bin -f episode.wav -otxt -osrt -ovtt
# Outputs: episode.txt, episode.srt, episode.vtt

OpenAI API:

from openai import OpenAI
client = OpenAI()

with open("episode.mp3", "rb") as f:
    transcript = client.audio.transcriptions.create(
        model="whisper-1",
        file=f,
        response_format="verbose_json",
        timestamp_granularities=["segment", "word"],
    )

print(transcript.text)
for segment in transcript.segments:
    print(f"[{segment.start:.1f}s] {segment.text}")

Step 3: Subtitle Generation (SRT/VTT)

Generate SRT with faster-whisper:

from faster_whisper import WhisperModel

model = WhisperModel("small", device="cpu", compute_type="int8")
segments, info = model.transcribe("episode.mp3", beam_size=5)

def format_timestamp(seconds):
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = int(seconds % 60)
    ms = int((seconds % 1) * 1000)
    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"

with open("episode.srt", "w") as f:
    for i, seg in enumerate(segments, 1):
        f.write(f"{i}\n")
        f.write(f"{format_timestamp(seg.start)} --> {format_timestamp(seg.end)}\n")
        f.write(f"{seg.text.strip()}\n\n")

print("Generated episode.srt")

Generate VTT (for HTML5 video):

with open("episode.vtt", "w") as f:
    f.write("WEBVTT\n\n")
    for i, seg in enumerate(segments, 1):
        start = format_timestamp(seg.start).replace(",", ".")
        end = format_timestamp(seg.end).replace(",", ".")
        f.write(f"{start} --> {end}\n")
        f.write(f"{seg.text.strip()}\n\n")

Word-level timestamps (for karaoke-style subtitles):

segments, info = model.transcribe("episode.mp3", word_timestamps=True)
for segment in segments:
    for word in segment.words:
        print(f"  [{word.start:.2f}s → {word.end:.2f}s] {word.word}")

Step 4: Language Detection & Translation

# Auto-detect language
segments, info = model.transcribe("foreign_audio.mp3")
print(f"Detected: {info.language} ({info.language_probability:.0%})")

# Force specific language
segments, info = model.transcribe("german.mp3", language="de")

# Translate to English (any language → English)
segments, info = model.transcribe("german.mp3", task="translate")
for seg in segments:
    print(seg.text)  # English translation

Supported languages: 99 languages including en, zh, de, es, ru, ko, fr, ja, pt, tr, pl, nl, ar, sv, it, hi, and many more.

Step 5: Speaker Diarization

Combine Whisper with pyannote-audio for "who said what":

from faster_whisper import WhisperModel
from pyannote.audio import Pipeline
import torch

# Transcribe
model = WhisperModel("small", device="cpu", compute_type="int8")
segments, info = model.transcribe("meeting.wav", beam_size=5)
whisper_segments = list(segments)

# Diarize (requires HuggingFace token with pyannote access)
diarization = Pipeline.from_pretrained(
    "pyannote/speaker-diarization-3.1",
    use_auth_token="hf_YOUR_TOKEN"
)
diarization_result = diarization("meeting.wav")

# Merge: assign speaker to each whisper segment
def get_speaker(start, end, diarization_result):
    """Find the dominant speaker during a time range."""
    speakers = {}
    for turn, _, speaker in diarization_result.itertracks(yield_label=True):
        overlap_start = max(start, turn.start)
        overlap_end = min(end, turn.end)
        if overlap_start < overlap_end:
            duration = overlap_end - overlap_start
            speakers[speaker] = speakers.get(speaker, 0) + duration
    return max(speakers, key=speakers.get) if speakers else "Unknown"

for seg in whisper_segments:
    speaker = get_speaker(seg.start, seg.end, diarization_result)
    print(f"[{speaker}] [{seg.start:.1f}s → {seg.end:.1f}s] {seg.text}")

Step 6: Batch Processing & Pipelines

Transcribe all episodes in a directory:

import os
from pathlib import Path
from faster_whisper import WhisperModel

model = WhisperModel("small", device="cpu", compute_type="int8")
episodes_dir = Path("episodes")
output_dir = Path("transcripts")
output_dir.mkdir(exist_ok=True)

for audio_file in sorted(episodes_dir.glob("*.mp3")):
    print(f"Transcribing: {audio_file.name}")
    segments, info = model.transcribe(str(audio_file), beam_size=5)
    
    # Plain text
    txt_path = output_dir / f"{audio_file.stem}.txt"
    with open(txt_path, "w") as f:
        for seg in segments:
            f.write(seg.text.strip() + "\n")
    
    # SRT
    srt_path = output_dir / f"{audio_file.stem}.srt"
    segments2, _ = model.transcribe(str(audio_file), beam_size=5)  # Re-iterate
    with open(srt_path, "w") as f:
        for i, seg in enumerate(segments2, 1):
            h, m = divmod(int(seg.start), 3600)
            m, s = divmod(m, 60)
            ms = int((seg.start % 1) * 1000)
            start_ts = f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
            h2, m2 = divmod(int(seg.end), 3600)
            m2, s2 = divmod(m2, 60)
            ms2 = int((seg.end % 1) * 1000)
            end_ts = f"{h2:02d}:{m2:02d}:{s2:02d},{ms2:03d}"
            f.write(f"{i}\n{start_ts} --> {end_ts}\n{seg.text.strip()}\n\n")
    
    print(f"  → {txt_path}, {srt_path}")

Step 7: Model Selection Guide

Model	Size	VRAM	Speed (CPU)	Accuracy	Best For
tiny	39M	~1GB	Very fast	Low	Quick drafts, real-time
base	74M	~1GB	Fast	Medium	Good balance for CPU
small	244M	~2GB	Moderate	Good	Podcasts, clear audio
medium	769M	~5GB	Slow	Very good	Noisy audio, accents
large-v3	1.5G	~10GB	Very slow	Best	Production quality

Recommendations:

• CPU-only, speed matters: tiny or base with faster-whisper
• CPU-only, accuracy matters: small with faster-whisper
• GPU available: large-v3 with faster-whisper (float16)
• No local compute: OpenAI API (whisper-1)

Examples

Example 1: Transcribe a podcast season and generate SRT subtitles

User prompt: "Transcribe all 20 MP3 episodes in ./episodes/ and generate both plain text transcripts and SRT subtitle files. I have a GPU with 10GB VRAM."

The agent will:

1. Install faster-whisper via pip install faster-whisper.
2. Load the large-v3 model with device="cuda" and compute_type="float16" to leverage the GPU.
3. Create a ./transcripts/ output directory.
4. Loop over all .mp3 files in ./episodes/, transcribing each with beam_size=5.
5. Write both a .txt file (plain text) and a .srt file (with properly formatted timestamps) for each episode.
6. Report the detected language and total processing time per episode.

Example 2: Translate a German interview to English with speaker labels

User prompt: "I have a 45-minute German interview recording at meeting.wav with two speakers. Transcribe it in English and label who said what."

The agent will:

1. Install faster-whisper and pyannote-audio (pip install faster-whisper pyannote.audio).
2. Transcribe meeting.wav with task="translate" to get English text from the German audio.
3. Run pyannote speaker diarization to identify speaker segments (requires a HuggingFace token with pyannote model access).
4. Merge Whisper segments with diarization results by matching time ranges to assign speaker labels.
5. Output a formatted transcript with [Speaker 1] and [Speaker 2] labels before each segment.

Guidelines

• Use faster-whisper over the original OpenAI Whisper for local transcription; it is 4x faster and uses less memory through INT8 quantization via CTranslate2.
• Select model size based on your hardware: tiny/base for CPU speed, small for CPU accuracy, large-v3 for GPU production quality.
• Always convert audio to WAV or ensure ffmpeg is installed when working with MP3/M4A inputs; Whisper relies on ffmpeg for non-WAV format decoding.
• For speaker diarization, the pyannote pipeline requires a HuggingFace access token with accepted model terms; set this up before attempting multi-speaker transcription.
• When generating SRT files, use beam_size=5 for more accurate segment boundaries; the default greedy decoding can produce poorly timed subtitle breaks.