How can you build a prominent pipeline Ai with whisprent, alignment, analysis, and export?
In this lesson, we walk in the advanced implementation of the SpperXWhen we examine the text, alignment, and the Word-Level TimesStamp in detail. We set up the environment, loading and pre-noise, and runs a full pipe, from writing and analysis, while verifying memory and supporting batch processing. On the way, we can see the consequences, send it to many formats, and remove keywords to find a deep understanding of audio content. Look Full codes here.
!pip install -q git+
!pip install -q pandas matplotlib seaborn
import whisperx
import torch
import gc
import os
import json
import pandas as pd
from pathlib import Path
from IPython.display import Audio, display, HTML
import warnings
warnings.filterwarnings('ignore')
CONFIG = {
"device": "cuda" if torch.cuda.is_available() else "cpu",
"compute_type": "float16" if torch.cuda.is_available() else "int8",
"batch_size": 16,
"model_size": "base",
"language": None,
}
print(f"🚀 Running on: {CONFIG['device']}")
print(f"📊 Compute type: {CONFIG['compute_type']}")
print(f"🎯 Model: {CONFIG['model_size']}")We start by installing Whispx and key libraries and prepares to set up our setup. We find that CaDa is available, select a computer type, and set the parameters as a batch size, the size of the model, and language to prepare text. Look Full codes here.
def download_sample_audio():
"""Download a sample audio file for testing"""
!wget -q -O sample.mp3
print("✅ Sample audio downloaded")
return "sample.mp3"
def load_and_analyze_audio(audio_path):
"""Load audio and display basic info"""
audio = whisperx.load_audio(audio_path)
duration = len(audio) / 16000
print(f"📁 Audio: {Path(audio_path).name}")
print(f"⏱️ Duration: {duration:.2f} seconds")
print(f"🎵 Sample rate: 16000 Hz")
display(Audio(audio_path))
return audio, duration
def transcribe_audio(audio, model_size=CONFIG["model_size"], language=None):
"""Transcribe audio using WhisperX (batched inference)"""
print("n🎤 STEP 1: Transcribing audio...")
model = whisperx.load_model(
model_size,
CONFIG["device"],
compute_type=CONFIG["compute_type"]
)
transcribe_kwargs = {
"batch_size": CONFIG["batch_size"]
}
if language:
transcribe_kwargs["language"] = language
result = model.transcribe(audio, **transcribe_kwargs)
total_segments = len(result["segments"])
total_words = sum(len(seg.get("words", [])) for seg in result["segments"])
del model
gc.collect()
if CONFIG["device"] == "cuda":
torch.cuda.empty_cache()
print(f"✅ Transcription complete!")
print(f" Language: {result['language']}")
print(f" Segments: {total_segments}")
print(f" Total text length: {sum(len(seg['text']) for seg in result['segments'])} characters")
return resultWe download a sample sound file, upload to analyze, and then pass on using WHSPHSPX. We have planned the compilation of our selected model size and configuration, and releases important information such as language, the number of categories, and complete text. Look Full codes here.
🚨 [Recommended Read] Vipe (Video Pose Pose): A Powerful and Powerful Tool of Video 3D video of AI
def align_transcription(segments, audio, language_code):
"""Align transcription for accurate word-level timestamps"""
print("n🎯 STEP 2: Aligning for word-level timestamps...")
try:
model_a, metadata = whisperx.load_align_model(
language_code=language_code,
device=CONFIG["device"]
)
result = whisperx.align(
segments,
model_a,
metadata,
audio,
CONFIG["device"],
return_char_alignments=False
)
total_words = sum(len(seg.get("words", [])) for seg in result["segments"])
del model_a
gc.collect()
if CONFIG["device"] == "cuda":
torch.cuda.empty_cache()
print(f"✅ Alignment complete!")
print(f" Aligned words: {total_words}")
return result
except Exception as e:
print(f"⚠️ Alignment failed: {str(e)}")
print(" Continuing with segment-level timestamps only...")
return {"segments": segments, "word_segments": []}We adapt to the text to produce direct timestamps of the level. By uploading the alignment model and uses the sound, we analyze time to time, and report the words to understand while memory confirmation is effective processing. Look Full codes here.
def analyze_transcription(result):
"""Generate statistics about the transcription"""
print("n📊 TRANSCRIPTION STATISTICS")
print("="*70)
segments = result["segments"]
total_duration = max(seg["end"] for seg in segments) if segments else 0
total_words = sum(len(seg.get("words", [])) for seg in segments)
total_chars = sum(len(seg["text"].strip()) for seg in segments)
print(f"Total duration: {total_duration:.2f} seconds")
print(f"Total segments: {len(segments)}")
print(f"Total words: {total_words}")
print(f"Total characters: {total_chars}")
if total_duration > 0:
print(f"Words per minute: {(total_words / total_duration * 60):.1f}")
pauses = []
for i in range(len(segments) - 1):
pause = segments[i+1]["start"] - segments[i]["end"]
if pause > 0:
pauses.append(pause)
if pauses:
print(f"Average pause between segments: {sum(pauses)/len(pauses):.2f}s")
print(f"Longest pause: {max(pauses):.2f}s")
word_durations = []
for seg in segments:
if "words" in seg:
for word in seg["words"]:
duration = word["end"] - word["start"]
word_durations.append(duration)
if word_durations:
print(f"Average word duration: {sum(word_durations)/len(word_durations):.3f}s")
print("="*70)It is analyzing of the written evaluation by detailed statistics such as full time, counting, word calculation, and calculations. We have also calculated words in a minute, setting a while between the parts, and the middle time in the word to better understand the flow of the noise. Look Full codes here.
def display_results(result, show_words=False, max_rows=50):
"""Display transcription results in formatted table"""
data = []
for seg in result["segments"]:
text = seg["text"].strip()
start = f"{seg['start']:.2f}s"
end = f"{seg['end']:.2f}s"
duration = f"{seg['end'] - seg['start']:.2f}s"
if show_words and "words" in seg:
for word in seg["words"]:
data.append({
"Start": f"{word['start']:.2f}s",
"End": f"{word['end']:.2f}s",
"Duration": f"{word['end'] - word['start']:.3f}s",
"Text": word["word"],
"Score": f"{word.get('score', 0):.2f}"
})
else:
data.append({
"Start": start,
"End": end,
"Duration": duration,
"Text": text
})
df = pd.DataFrame(data)
if len(df) > max_rows:
print(f"Showing first {max_rows} rows of {len(df)} total...")
display(HTML(df.head(max_rows).to_html(index=False)))
else:
display(HTML(df.to_html(index=False)))
return df
def export_results(result, output_dir="output", filename="transcript"):
"""Export results in multiple formats"""
os.makedirs(output_dir, exist_ok=True)
json_path = f"{output_dir}/{filename}.json"
with open(json_path, "w", encoding="utf-8") as f:
json.dump(result, f, indent=2, ensure_ascii=False)
srt_path = f"{output_dir}/{filename}.srt"
with open(srt_path, "w", encoding="utf-8") as f:
for i, seg in enumerate(result["segments"], 1):
start = format_timestamp(seg["start"])
end = format_timestamp(seg["end"])
f.write(f"{i}n{start} --> {end}n{seg['text'].strip()}nn")
vtt_path = f"{output_dir}/{filename}.vtt"
with open(vtt_path, "w", encoding="utf-8") as f:
f.write("WEBVTTnn")
for i, seg in enumerate(result["segments"], 1):
start = format_timestamp_vtt(seg["start"])
end = format_timestamp_vtt(seg["end"])
f.write(f"{start} --> {end}n{seg['text'].strip()}nn")
txt_path = f"{output_dir}/{filename}.txt"
with open(txt_path, "w", encoding="utf-8") as f:
for seg in result["segments"]:
f.write(f"{seg['text'].strip()}n")
csv_path = f"{output_dir}/{filename}.csv"
df_data = []
for seg in result["segments"]:
df_data.append({
"start": seg["start"],
"end": seg["end"],
"text": seg["text"].strip()
})
pd.DataFrame(df_data).to_csv(csv_path, index=False)
print(f"n💾 Results exported to '{output_dir}/' directory:")
print(f" ✓ {filename}.json (full structured data)")
print(f" ✓ {filename}.srt (subtitles)")
print(f" ✓ {filename}.vtt (web video subtitles)")
print(f" ✓ {filename}.txt (plain text)")
print(f" ✓ {filename}.csv (timestamps + text)")
def format_timestamp(seconds):
"""Convert seconds to SRT timestamp format"""
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
secs = int(seconds % 60)
millis = int((seconds % 1) * 1000)
return f"{hours:02d}:{minutes:02d}:{secs:02d},{millis:03d}"
def format_timestamp_vtt(seconds):
"""Convert seconds to VTT timestamp format"""
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
secs = int(seconds % 60)
millis = int((seconds % 1) * 1000)
return f"{hours:02d}:{minutes:02d}:{secs:02d}.{millis:03d}"
def batch_process_files(audio_files, output_dir="batch_output"):
"""Process multiple audio files in batch"""
print(f"n📦 Batch processing {len(audio_files)} files...")
results = {}
for i, audio_path in enumerate(audio_files, 1):
print(f"n[{i}/{len(audio_files)}] Processing: {Path(audio_path).name}")
try:
result, _ = process_audio_file(audio_path, show_output=False)
results[audio_path] = result
filename = Path(audio_path).stem
export_results(result, output_dir, filename)
except Exception as e:
print(f"❌ Error processing {audio_path}: {str(e)}")
results[audio_path] = None
print(f"n✅ Batch processing complete! Processed {len(results)} files.")
return results
def extract_keywords(result, top_n=10):
"""Extract most common words from transcription"""
from collections import Counter
import re
text = " ".join(seg["text"] for seg in result["segments"])
words = re.findall(r'bw+b', text.lower())
stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for',
'of', 'with', 'is', 'was', 'are', 'were', 'be', 'been', 'being',
'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could',
'should', 'may', 'might', 'must', 'can', 'this', 'that', 'these', 'those'}
filtered_words = [w for w in words if w not in stop_words and len(w) > 2]
word_counts = Counter(filtered_words).most_common(top_n)
print(f"n🔑 Top {top_n} Keywords:")
for word, count in word_counts:
print(f" {word}: {count}")
return word_countsWe put the results on clean tables, ship without JSON / SRT / TXT / TXT / CSV formats, and maintain the exact time of CSV, and maintain the exact time of helseters in helpful Helsettes. We have also processed many of the end files of the end of the end of the end and releases high quality, enabling us to change the green text into the right artistic art. Look Full codes here.
def process_audio_file(audio_path, show_output=True, analyze=True):
"""Complete WhisperX pipeline"""
if show_output:
print("="*70)
print("🎵 WhisperX Advanced Tutorial")
print("="*70)
audio, duration = load_and_analyze_audio(audio_path)
result = transcribe_audio(audio, CONFIG["model_size"], CONFIG["language"])
aligned_result = align_transcription(
result["segments"],
audio,
result["language"]
)
if analyze and show_output:
analyze_transcription(aligned_result)
extract_keywords(aligned_result)
if show_output:
print("n" + "="*70)
print("📋 TRANSCRIPTION RESULTS")
print("="*70)
df = display_results(aligned_result, show_words=False)
export_results(aligned_result)
else:
df = None
return aligned_result, df
# Example 1: Process sample audio
# audio_path = download_sample_audio()
# result, df = process_audio_file(audio_path)
# Example 2: Show word-level details
# result, df = process_audio_file(audio_path)
# word_df = display_results(result, show_words=True)
# Example 3: Process your own audio
# audio_path = "your_audio.wav" # or .mp3, .m4a, etc.
# result, df = process_audio_file(audio_path)
# Example 4: Batch process multiple files
# audio_files = ["audio1.mp3", "audio2.wav", "audio3.m4a"]
# results = batch_process_files(audio_files)
# Example 5: Use a larger model for better accuracy
# CONFIG["model_size"] = "large-v2"
# result, df = process_audio_file("audio.mp3")
print("n✨ Setup complete! Uncomment examples above to run.")We run the perfect whippex Pipeline at the end, wepload audio, transmit it, and we are synchronized at times of the word level. When we are enabled, we can analyze the statistics, issue keywords, and offer a table of clean results, and send everything to multiple formats, ready for real use.
In conclusion, we build a complete complete pipe that is not only a sound but also directing the direct Timestamps of the level. We produce results in many formats, batches processing files, and analyze patterns to do what is more meaningful. During this time, we now have flexible work, ready for the use of text text and sound analysis in Colob, and we are ready to pass it on to real world projects.
Look Full codes here. Feel free to look our GITHUB page for tutorials, codes and letters of writing. Also, feel free to follow it Sane and don't forget to join ours 100K + ml subreddit Then sign up for Our newspaper. Wait! Do you with a telegram? Now you can join us with a telegram.
Asphazzaq is a Markteach Media Inc. According to a View Business and Developer, Asifi is committed to integrating a good social intelligence. His latest attempt is launched by the launch of the chemistrylife plan for an intelligence, MarktechPost, a devastating intimate practice of a machine learning and deep learning issues that are clearly and easily understood. The platform is adhering to more than two million moon visits, indicating its popularity between the audience.
🔥[Recommended Read] NVIDIA AI Open-Spaces Vipe (Video Video Engine): A Powerful and Powerful Tool to Enter the 3D Reference for 3D for Spatial Ai
