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import whisperx
import torch
import numpy as np
from scipy.signal import resample
import numpy as np
import whisperx
from pyannote.audio import Pipeline
import os
from dotenv import load_dotenv
load_dotenv()
hf_token = os.getenv("HF_TOKEN")
import whisperx
import torch
import numpy as np
import whisperx
import torch
import numpy as np
import whisperx
import torch
import numpy as np
CHUNK_LENGTH=5
# def process_audio(audio_file):
# device = "cuda" if torch.cuda.is_available() else "cpu"
# compute_type = "float32"
# audio = whisperx.load_audio(audio_file)
# model = whisperx.load_model("small", device, compute_type=compute_type)
# # Initial transcription
# result = model.transcribe(audio, batch_size=8)
# # Sliding window for language detection
# window_size = 5 # seconds
# step_size = 1 # seconds
# sample_rate = 16000
# language_probs = []
# audio_duration = len(audio) / sample_rate
# if audio_duration <= window_size:
# # If audio is shorter than or equal to window size, detect language for entire audio
# lang = model.detect_language(audio)
# language_probs.append((0, lang))
# else:
# for i in range(0, len(audio) - window_size * sample_rate + 1, step_size * sample_rate):
# window = audio[i:i + window_size * sample_rate]
# lang = model.detect_language(window)
# language_probs.append((i / sample_rate, lang))
# # Detect language changes
# language_segments = []
# current_lang = language_probs[0][1]
# start_time = 0
# for time, lang in language_probs[1:]:
# if lang != current_lang:
# language_segments.append({
# "language": current_lang,
# "start": start_time,
# "end": time
# })
# current_lang = lang
# start_time = time
# # Add the last segment
# language_segments.append({
# "language": current_lang,
# "start": start_time,
# "end": audio_duration
# })
# # Re-transcribe each language segment
# final_segments = []
# for segment in language_segments:
# start_sample = int(segment["start"] * sample_rate)
# end_sample = int(segment["end"] * sample_rate)
# segment_audio = audio[start_sample:end_sample]
# segment_result = model.transcribe(segment_audio, language=segment["language"])
# for seg in segment_result["segments"]:
# seg["start"] += segment["start"]
# seg["end"] += segment["start"]
# seg["language"] = segment["language"]
# final_segments.append(seg)
# return language_segments, final_segments
import whisperx
import torch
import numpy as np
def preprocess_audio(audio, chunk_size=CHUNK_LENGTH*16000): # 30 seconds at 16kHz
chunks = []
for i in range(0, len(audio), chunk_size):
chunk = audio[i:i+chunk_size]
if len(chunk) < chunk_size:
chunk = np.pad(chunk, (0, chunk_size - len(chunk)))
chunks.append(chunk)
return chunks
def process_audio(audio_file):
device = "cuda" if torch.cuda.is_available() else "cpu"
compute_type = "float32"
audio = whisperx.load_audio(audio_file)
model = whisperx.load_model("small", device, compute_type=compute_type)
# Initialize speaker diarization pipeline
diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=hf_token)
diarization_pipeline = diarization_pipeline.to(torch.device(device))
# Perform diarization on the entire audio
diarization_result = diarization_pipeline({"waveform": torch.from_numpy(audio).unsqueeze(0), "sample_rate": 16000})
# Preprocess audio into consistent chunks
chunks = preprocess_audio(audio)
language_segments = []
final_segments = []
for i, chunk in enumerate(chunks):
# Detect language for this chunk
lang = model.detect_language(chunk)
# Transcribe this chunk
result = model.transcribe(chunk, language=lang)
chunk_start_time = i * 5 # Each chunk is 30 seconds
# Adjust timestamps and add language information
for segment in result["segments"]:
segment_start = chunk_start_time + segment["start"]
segment_end = chunk_start_time + segment["end"]
segment["start"] = segment_start
segment["end"] = segment_end
segment["language"] = lang
speakers = []
for turn, track, speaker in diarization_result.itertracks(yield_label=True):
if turn.start <= segment_end and turn.end >= segment_start:
speakers.append(speaker)
if speakers:
segment["speaker"] = max(set(speakers), key=speakers.count)
else:
segment["speaker"] = "Unknown"
final_segments.append(segment)
# Add language segment
language_segments.append({
"language": lang,
"start": chunk_start_time,
"end": chunk_start_time + 5
})
return language_segments, final_segments
def print_results(language, language_probs, segments):
print(f"Detected Language: {language}")
print("Language Probabilities:")
for lang, prob in language_probs.items():
print(f" {lang}: {prob:.4f}")
print("\nTranscription:")
for segment in segments:
print(f"[{segment['start']:.2f}s - {segment['end']:.2f}s] Speaker {segment['speaker']}: {segment['text']}") |