import numpy as np
import cvxpy as cp
import re
import copy
import concurrent.futures
import gradio as gr
from datetime import datetime
import random
import moviepy
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
from moviepy.editor import (
ImageClip,
VideoFileClip,
TextClip,
CompositeVideoClip,
CompositeAudioClip,
AudioFileClip,
concatenate_videoclips,
concatenate_audioclips
)
from PIL import Image, ImageDraw, ImageFont
from moviepy.audio.AudioClip import AudioArrayClip
import subprocess
import json
import logging
import whisperx
import time
import os
import openai
from openai import OpenAI
import traceback
from TTS.api import TTS
import torch
from pydub import AudioSegment
from pyannote.audio import Pipeline
import traceback
import wave
logger = logging.getLogger(__name__)
# Configure logging
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
logger.info(f"MoviePy Version: {moviepy.__version__}")
# Accept license terms for Coqui XTTS
os.environ["COQUI_TOS_AGREED"] = "1"
# torch.serialization.add_safe_globals([XttsConfig])
logger.info(gr.__version__)
client = OpenAI(
api_key= os.environ.get("openAI_api_key"), # This is the default and can be omitted
)
hf_api_key = os.environ.get("hf_token")
def silence(duration, fps=44100):
"""
Returns a silent AudioClip of the specified duration.
"""
return AudioArrayClip(np.zeros((int(fps*duration), 2)), fps=fps)
def count_words_or_characters(text):
# Count non-Chinese words
non_chinese_words = len(re.findall(r'\b[a-zA-Z0-9]+\b', text))
# Count Chinese characters
chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
return non_chinese_words + chinese_chars
# Define the passcode
PASSCODE = "show_feedback_db"
css = """
/* Adjust row height */
.dataframe-container tr {
height: 50px !important;
}
/* Ensure text wrapping and prevent overflow */
.dataframe-container td {
white-space: normal !important;
word-break: break-word !important;
}
/* Set column widths */
[data-testid="block-container"] .scrolling-dataframe th:nth-child(1),
[data-testid="block-container"] .scrolling-dataframe td:nth-child(1) {
width: 6%; /* Start column */
}
[data-testid="block-container"] .scrolling-dataframe th:nth-child(2),
[data-testid="block-container"] .scrolling-dataframe td:nth-child(2) {
width: 47%; /* Original text */
}
[data-testid="block-container"] .scrolling-dataframe th:nth-child(3),
[data-testid="block-container"] .scrolling-dataframe td:nth-child(3) {
width: 47%; /* Translated text */
}
[data-testid="block-container"] .scrolling-dataframe th:nth-child(4),
[data-testid="block-container"] .scrolling-dataframe td:nth-child(4) {
display: none !important;
}
"""
# Function to save feedback or provide access to the database file
def handle_feedback(feedback):
feedback = feedback.strip() # Clean up leading/trailing whitespace
if not feedback:
return "Feedback cannot be empty.", None
if feedback == PASSCODE:
# Provide access to the feedback.db file
return "Access granted! Download the database file below.", "feedback.db"
else:
# Save feedback to the database
with sqlite3.connect("feedback.db") as conn:
cursor = conn.cursor()
cursor.execute("CREATE TABLE IF NOT EXISTS studio_feedback (id INTEGER PRIMARY KEY, comment TEXT)")
cursor.execute("INSERT INTO studio_feedback (comment) VALUES (?)", (feedback,))
conn.commit()
return "Thank you for your feedback!", None
def segment_background_audio(audio_path, background_audio_path="background_segments.wav"):
"""
Uses Demucs to separate audio and extract background (non-vocal) parts.
Merges drums, bass, and other stems into a single background track.
"""
# Step 1: Run Demucs using the 4-stem model
subprocess.run([
"demucs",
"-n", "htdemucs", # 4-stem model
audio_path
], check=True)
# Step 2: Locate separated stem files
filename = os.path.splitext(os.path.basename(audio_path))[0]
stem_dir = os.path.join("separated", "htdemucs", filename)
# Step 3: Load and merge background stems
drums = AudioSegment.from_wav(os.path.join(stem_dir, "drums.wav"))
bass = AudioSegment.from_wav(os.path.join(stem_dir, "bass.wav"))
other = AudioSegment.from_wav(os.path.join(stem_dir, "other.wav"))
background = drums.overlay(bass).overlay(other)
# Step 4: Export the merged background
background.export(background_audio_path, format="wav")
return background_audio_path
# def segment_background_audio(audio_path, background_audio_path="background_segments.wav"):
# pipeline = Pipeline.from_pretrained("pyannote/voice-activity-detection", use_auth_token=hf_api_key)
# vad_result = pipeline(audio_path)
# full_audio = AudioSegment.from_wav(audio_path)
# full_duration_sec = len(full_audio) / 1000.0
# current_time = 0.0
# result_audio = AudioSegment.empty()
# for segment in vad_result.itersegments():
# # Background segment before the speech
# if current_time < segment.start:
# bg = full_audio[int(current_time * 1000):int(segment.start * 1000)]
# result_audio += bg
# # Add silence for the speech duration
# silence_duration = segment.end - segment.start
# result_audio += AudioSegment.silent(duration=int(silence_duration * 1000))
# current_time = segment.end
# # Handle any remaining background after the last speech
# if current_time < full_duration_sec:
# result_audio += full_audio[int(current_time * 1000):]
# result_audio.export(background_audio_path, format="wav")
# return background_audio_path
def transcribe_video_with_speakers(video_path):
# Extract audio from video
video = VideoFileClip(video_path)
audio_path = "audio.wav"
video.audio.write_audiofile(audio_path)
logger.info(f"Audio extracted from video: {audio_path}")
segment_result = segment_background_audio(audio_path)
print(f"Saved non-speech (background) audio to local")
# Set up device
device = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Using device: {device}")
try:
# Load a medium model with float32 for broader compatibility
model = whisperx.load_model("large-v3", device=device, compute_type="float32")
logger.info("WhisperX model loaded")
# Transcribe
result = model.transcribe(audio_path, chunk_size=6, print_progress = True)
logger.info("Audio transcription completed")
# Get the detected language
detected_language = result["language"]
logger.debug(f"Detected language: {detected_language}")
# Alignment
model_a, metadata = whisperx.load_align_model(language_code=result["language"], device=device)
result = whisperx.align(result["segments"], model_a, metadata, audio_path, device)
logger.info("Transcription alignment completed")
# Diarization (works independently of Whisper model size)
diarize_model = whisperx.DiarizationPipeline(use_auth_token=hf_api_key, device=device)
diarize_segments = diarize_model(audio_path)
logger.info("Speaker diarization completed")
# Assign speakers
result = whisperx.assign_word_speakers(diarize_segments, result)
logger.info("Speakers assigned to transcribed segments")
except Exception as e:
logger.error(f"❌ WhisperX pipeline failed: {e}")
# Extract timestamps, text, and speaker IDs
transcript_with_speakers = [
{
"start": segment["start"],
"end": segment["end"],
"text": segment["text"],
"speaker": segment.get("speaker", "SPEAKER_00")
}
for segment in result["segments"]
]
# Collect audio for each speaker
speaker_audio = {}
for segment in result["segments"]:
speaker = segment.get("speaker", "SPEAKER_00")
if speaker not in speaker_audio:
speaker_audio[speaker] = []
speaker_audio[speaker].append((segment["start"], segment["end"]))
# Collapse and truncate speaker audio
speaker_sample_paths = {}
audio_clip = AudioFileClip(audio_path)
for speaker, segments in speaker_audio.items():
speaker_clips = [audio_clip.subclip(start, end) for start, end in segments]
combined_clip = concatenate_audioclips(speaker_clips)
truncated_clip = combined_clip.subclip(0, min(30, combined_clip.duration))
sample_path = f"speaker_{speaker}_sample.wav"
truncated_clip.write_audiofile(sample_path)
speaker_sample_paths[speaker] = sample_path
logger.info(f"Created sample for {speaker}: {sample_path}")
# Clean up
video.close()
audio_clip.close()
os.remove(audio_path)
return transcript_with_speakers, detected_language
# Function to get the appropriate translation model based on target language
def get_translation_model(source_language, target_language):
"""
Get the translation model based on the source and target language.
Parameters:
- target_language (str): The language to translate the content into (e.g., 'es', 'fr').
- source_language (str): The language of the input content (default is 'en' for English).
Returns:
- str: The translation model identifier.
"""
# List of allowable languages
allowable_languages = ["en", "es", "fr", "zh", "de", "it", "pt", "ja", "ko", "ru", "hi", "tr"]
# Validate source and target languages
if source_language not in allowable_languages:
logger.debug(f"Invalid source language '{source_language}'. Supported languages are: {', '.join(allowable_languages)}")
# Return a default model if source language is invalid
source_language = "en" # Default to 'en'
if target_language not in allowable_languages:
logger.debug(f"Invalid target language '{target_language}'. Supported languages are: {', '.join(allowable_languages)}")
# Return a default model if target language is invalid
target_language = "zh" # Default to 'zh'
if source_language == target_language:
source_language = "en" # Default to 'en'
target_language = "zh" # Default to 'zh'
# Return the model using string concatenation
return f"Helsinki-NLP/opus-mt-{source_language}-{target_language}"
def translate_single_entry(entry, translator):
original_text = entry["text"]
translated_text = translator(original_text)[0]['translation_text']
return {
"start": entry["start"],
"original": original_text,
"translated": translated_text,
"end": entry["end"],
"speaker": entry["speaker"]
}
def translate_text(transcription_json, source_language, target_language):
# Load the translation model for the specified target language
translation_model_id = get_translation_model(source_language, target_language)
logger.debug(f"Translation model: {translation_model_id}")
translator = pipeline("translation", model=translation_model_id)
# Use ThreadPoolExecutor to parallelize translations
with concurrent.futures.ThreadPoolExecutor() as executor:
# Submit all translation tasks and collect results
translate_func = lambda entry: translate_single_entry(entry, translator)
translated_json = list(executor.map(translate_func, transcription_json))
# Sort the translated_json by start time
translated_json.sort(key=lambda x: x["start"])
# Log the components being added to translated_json
for entry in translated_json:
logger.debug("Added to translated_json: start=%s, original=%s, translated=%s, end=%s, speaker=%s",
entry["start"], entry["original"], entry["translated"], entry["end"], entry["speaker"])
return translated_json
def update_translations(file, edited_table, process_mode):
"""
Update the translations based on user edits in the Gradio Dataframe.
"""
output_video_path = "output_video.mp4"
logger.debug(f"Editable Table: {edited_table}")
if file is None:
logger.info("No file uploaded. Please upload a video/audio file.")
return None, [], None, "No file uploaded. Please upload a video/audio file."
try:
start_time = time.time() # Start the timer
# Convert the edited_table (list of lists) back to list of dictionaries
updated_translations = [
{
"start": row["start"], # Access by column name
"original": row["original"],
"translated": row["translated"],
"end": row["end"]
}
for _, row in edited_table.iterrows()
]
# Call the function to process the video with updated translations
add_transcript_voiceover(file.name, updated_translations, output_video_path, process_mode)
# Calculate elapsed time
elapsed_time = time.time() - start_time
elapsed_time_display = f"Updates applied successfully in {elapsed_time:.2f} seconds."
return output_video_path, elapsed_time_display
except Exception as e:
raise ValueError(f"Error updating translations: {e}")
def create_subtitle_clip_pil(text, start_time, end_time, video_width, video_height, font_path):
try:
subtitle_width = int(video_width * 0.8)
aspect_ratio = video_height / video_width
subtitle_font_size = int(video_width // 22 if aspect_ratio > 1.2 else video_height // 24)
font = ImageFont.truetype(font_path, subtitle_font_size)
dummy_img = Image.new("RGBA", (subtitle_width, 1), (0, 0, 0, 0))
draw = ImageDraw.Draw(dummy_img)
# Word wrapping
lines = []
line = ""
for word in text.split():
test_line = f"{line} {word}".strip()
bbox = draw.textbbox((0, 0), test_line, font=font)
w = bbox[2] - bbox[0]
if w <= subtitle_width - 10:
line = test_line
else:
lines.append(line)
line = word
lines.append(line)
outline_width=2
line_heights = [draw.textbbox((0, 0), l, font=font)[3] - draw.textbbox((0, 0), l, font=font)[1] for l in lines]
total_height = sum(line_heights) + (len(lines) - 1) * 5 + 6 * outline_width
img = Image.new("RGBA", (subtitle_width, total_height), (0, 0, 0, 0))
draw = ImageDraw.Draw(img)
def draw_text_with_outline(draw, pos, text, font, fill="yellow", outline="black", outline_width = outline_width):
x, y = pos
# Draw outline
for dx in range(-outline_width, outline_width + 1):
for dy in range(-outline_width, outline_width + 1):
if dx != 0 or dy != 0:
draw.text((x + dx, y + dy), text, font=font, fill=outline)
# Draw main text
draw.text((x, y), text, font=font, fill=fill)
y = 0
for idx, line in enumerate(lines):
bbox = draw.textbbox((0, 0), line, font=font)
w = bbox[2] - bbox[0]
x = (subtitle_width - w) // 2
draw_text_with_outline(draw, (x, y), line, font)
y += line_heights[idx] + 5
img_np = np.array(img)
margin = int(video_height * 0.05)
img_clip = ImageClip(img_np) # Create the ImageClip first
image_height = img_clip.size[1]
txt_clip = (
img_clip # Use the already created clip
.set_start(start_time)
.set_duration(end_time - start_time)
.set_position(("center", video_height - image_height - margin))
.set_opacity(0.9)
)
return txt_clip
except Exception as e:
logger.error(f"❌ Failed to create subtitle clip: {e}")
return None
def solve_optimal_alignment(original_segments, generated_durations, total_duration):
"""
Aligns speech segments using quadratic programming. If optimization fails,
applies greedy fallback: center shorter segments, stretch longer ones.
Logs alignment results for traceability.
"""
N = len(original_segments)
d = np.array(generated_durations)
m = np.array([(seg['start'] + seg['end']) / 2 for seg in original_segments])
try:
s = cp.Variable(N)
objective = cp.Minimize(cp.sum_squares(s + d / 2 - m))
constraints = [s[0] >= 0]
for i in range(N - 1):
constraints.append(s[i] + d[i] <= s[i + 1])
constraints.append(s[N - 1] + d[N - 1] <= total_duration)
problem = cp.Problem(objective, constraints)
problem.solve()
if s.value is None:
raise ValueError("Solver failed")
for i in range(N):
original_segments[i]['start'] = round(s.value[i], 3)
original_segments[i]['end'] = round(s.value[i] + d[i], 3)
logger.info(
f"[OPT] Segment {i}: duration={d[i]:.2f}s | start={original_segments[i]['start']:.2f}s | "
f"end={original_segments[i]['end']:.2f}s | mid={m[i]:.2f}s"
)
except Exception as e:
logger.warning(f"⚠️ Optimization failed: {e}, falling back to greedy alignment.")
for i in range(N):
orig_start = original_segments[i]['start']
orig_end = original_segments[i]['end']
orig_mid = (orig_start + orig_end) / 2
gen_duration = generated_durations[i]
orig_duration = orig_end - orig_start
if gen_duration <= orig_duration:
new_start = orig_mid - gen_duration / 2
new_end = orig_mid + gen_duration / 2
else:
extra = (gen_duration - orig_duration) / 2
new_start = orig_start - extra
new_end = orig_end + extra
if i > 0:
prev_end = original_segments[i - 1]['end']
new_start = max(new_start, prev_end + 0.01)
if i < N - 1:
next_start = original_segments[i + 1]['start']
new_end = min(new_end, next_start - 0.01)
if new_end <= new_start:
new_start = orig_start
new_end = orig_start + gen_duration
original_segments[i]['start'] = round(new_start, 3)
original_segments[i]['end'] = round(new_end, 3)
logger.info(
f"[FALLBACK] Segment {i}: duration={gen_duration:.2f}s | start={new_start:.2f}s | "
f"end={new_end:.2f}s | original_mid={orig_mid:.2f}s"
)
return original_segments
def get_frame_image_bytes(video, t):
frame = video.get_frame(t)
img = Image.fromarray(frame)
buf = io.BytesIO()
img.save(buf, format='JPEG')
return buf.getvalue()
def post_edit_segment(entry, image_bytes):
try:
system_prompt = """You are a multilingual assistant helping polish subtitles and voiceover content.
Your job is to fix punctuation, validate meaning, improve tone, and ensure the translation matches the speaker's intended message."""
user_prompt = f"""
Original (source) transcript: {entry.get("original", "")}
Translated version: {entry.get("translated", "")}
Speaker ID: {entry.get("speaker", "")}
Time: {entry.get("start")} - {entry.get("end")}
Please:
1. Add correct punctuation and sentence boundaries.
2. Improve fluency and tone of the translated text.
3. Ensure the meaning is preserved from the original.
4. Use the attached image frame to infer emotion or setting.
Return the revised original and translated texts in the following format:
Original: <edited original>
Translated: <edited translation>
"""
response = ChatCompletion.create(
model="gpt-4o",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt, "image": image_bytes}
]
)
output = response.choices[0].message.content.strip()
lines = output.splitlines()
for line in lines:
if line.startswith("Original:"):
entry['original'] = line[len("Original:"):].strip()
elif line.startswith("Translated:"):
entry['translated'] = line[len("Translated:"):].strip()
return entry
except Exception as e:
print(f"Post-editing failed for segment: {e}")
return entry
def post_edit_translated_segments(translated_json, video_path):
video = VideoFileClip(video_path)
def process(entry):
mid_time = (entry['start'] + entry['end']) / 2
image_bytes = get_frame_image_bytes(video, mid_time)
entry = post_edit_segment(entry, image_bytes)
return entry
with concurrent.futures.ThreadPoolExecutor() as executor:
edited = list(executor.map(process, translated_json))
video.close()
return edited
def process_entry(entry, i, tts_model, video_width, video_height, process_mode, target_language, font_path, speaker_sample_paths=None):
logger.debug(f"Processing entry {i}: {entry}")
error_message = None
try:
txt_clip = create_subtitle_clip_pil(entry["translated"], entry["start"], entry["end"], video_width, video_height, font_path)
except Exception as e:
error_message = f"❌ Failed to create subtitle clip for entry {i}: {e}"
logger.error(error_message)
txt_clip = None
audio_segment = None
actual_duration = 0.0
if process_mode > 1:
try:
segment_audio_path = f"segment_{i}_voiceover.wav"
desired_duration = entry["end"] - entry["start"]
desired_speed = entry['speed'] #calibrated_speed(entry['translated'], desired_duration)
speaker = entry.get("speaker", "SPEAKER_00")
speaker_wav_path = f"speaker_{speaker}_sample.wav"
if process_mode > 2 and speaker_wav_path and os.path.exists(speaker_wav_path) and target_language in tts_model.synthesizer.tts_model.language_manager.name_to_id.keys():
generate_voiceover_clone(entry['translated'], tts_model, desired_speed, target_language, speaker_wav_path, segment_audio_path)
else:
generate_voiceover_OpenAI(entry['translated'], target_language, desired_speed, segment_audio_path)
if not segment_audio_path or not os.path.exists(segment_audio_path):
raise FileNotFoundError(f"Voiceover file not generated at: {segment_audio_path}")
audio_clip = AudioFileClip(segment_audio_path)
actual_duration = audio_clip.duration
audio_segment = audio_clip # Do not set start here, alignment happens later
except Exception as e:
err = f"❌ Failed to generate audio segment for entry {i}: {e}"
logger.error(err)
error_message = error_message + " | " + err if error_message else err
audio_segment = None
return i, txt_clip, audio_segment, actual_duration, error_message
def add_transcript_voiceover(video_path, translated_json, output_path, process_mode, target_language="en", speaker_sample_paths=None, background_audio_path="background_segments.wav"):
video = VideoFileClip(video_path)
font_path = "./NotoSansSC-Regular.ttf"
text_clips = []
audio_segments = []
actual_durations = []
error_messages = []
if process_mode > 2:
global tts_model
if tts_model is None:
try:
print("🔄 Loading XTTS model...")
from TTS.api import TTS
tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts")
print("✅ XTTS model loaded successfully.")
except Exception as e:
print("❌ Error loading XTTS model:")
traceback.print_exc()
return f"Error loading XTTS model: {e}"
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(process_entry, entry, i, tts_model, video.w, video.h, process_mode, target_language, font_path, speaker_sample_paths)
for i, entry in enumerate(translated_json)]
results = []
for future in concurrent.futures.as_completed(futures):
try:
i, txt_clip, audio_segment, actual_duration, error = future.result()
results.append((i, txt_clip, audio_segment, actual_duration))
if error:
error_messages.append(f"[Entry {i}] {error}")
except Exception as e:
err = f"❌ Unexpected error in future result: {e}"
error_messages.append(err)
results.sort(key=lambda x: x[0])
text_clips = [clip for _, clip, _, _ in results if clip]
generated_durations = [dur for _, _, _, dur in results if dur > 0]
# Align using optimization (modifies translated_json in-place)
translated_json = solve_optimal_alignment(translated_json, generated_durations, video.duration)
# Set aligned timings
audio_segments = []
for i, entry in enumerate(translated_json):
segment = results[i][2] # AudioFileClip
if segment:
segment = segment.set_start(entry['start']).set_duration(entry['end'] - entry['start'])
audio_segments.append(segment)
final_video = CompositeVideoClip([video] + text_clips)
if process_mode > 1 and audio_segments:
try:
voice_audio = CompositeAudioClip(audio_segments).set_duration(video.duration)
if background_audio_path and os.path.exists(background_audio_path):
background_audio = AudioFileClip(background_audio_path).set_duration(video.duration)
final_audio = CompositeAudioClip([voice_audio, background_audio])
else:
final_audio = voice_audio
final_video = final_video.set_audio(final_audio)
except Exception as e:
print(f"❌ Failed to set audio: {e}")
final_video.write_videofile(output_path, codec="libx264", audio_codec="aac")
return error_messages
def generate_voiceover_OpenAI(full_text, language, desired_speed, output_audio_path):
"""
Generate voiceover from translated text for a given language using OpenAI TTS API.
"""
# Define the voice based on the language (for now, use 'alloy' as default)
voice = "alloy" # Adjust based on language if needed
# Define the model (use tts-1 for real-time applications)
model = "tts-1"
max_retries = 3
retry_count = 0
while retry_count < max_retries:
try:
# Create the speech using OpenAI TTS API
response = client.audio.speech.create(
model=model,
voice=voice,
input=full_text,
speed=desired_speed
)
# Save the audio to the specified path
with open(output_audio_path, 'wb') as f:
for chunk in response.iter_bytes():
f.write(chunk)
logging.info(f"Voiceover generated successfully for {output_audio_path}")
break
except Exception as e:
retry_count += 1
logging.error(f"Error generating voiceover (retry {retry_count}/{max_retries}): {e}")
time.sleep(5) # Wait 5 seconds before retrying
if retry_count == max_retries:
raise ValueError(f"Failed to generate voiceover after {max_retries} retries.")
def generate_voiceover_clone(full_text, tts_model, desired_speed, target_language, speaker_wav_path, output_audio_path):
try:
tts_model.tts_to_file(
text=full_text,
speaker_wav=speaker_wav_path,
language=target_language,
file_path=output_audio_path,
speed=desired_speed,
split_sentences=True
)
msg = (
f"✅ Voice cloning completed successfully. "
f"[Speaker Wav: {speaker_wav_path}] [Speed: {desired_speed}]"
)
logger.info(msg)
return output_audio_path, msg, None
except Exception as e:
generate_voiceover_OpenAI(full_text, target_language, desired_speed, output_audio_path)
err_msg = f"❌ An error occurred: {str(e)}, fallback to premium voice"
logger.error(traceback.format_exc())
return None, err_msg, err_msg
def apply_adaptive_speed(translated_json_raw, source_language, target_language, k=3.0, default_prior_speed=5.0):
"""
Adds `speed` (relative, 1.0 = normal speed) and `target_duration` (sec) to each segment
using shrinkage-based estimation, language stretch ratios, and optional style modifiers.
Speeds are clamped to [0.85, 1.7] to avoid unnatural TTS behavior.
"""
translated_json = copy.deepcopy(translated_json_raw)
# Prior average speech speeds by (category, target language)
priors = {
("drama", "en"): 5.0,
("drama", "zh"): 4.5,
("tutorial", "en"): 5.2,
("tutorial", "zh"): 4.8,
("shortplay", "en"): 5.1,
("shortplay", "zh"): 4.7,
}
# Adjustment ratio based on language pair (source → target)
lang_ratio = {
("zh", "en"): 0.85,
("en", "zh"): 1.15,
("zh", "jp"): 1.05,
("en", "ja"): 0.9,
}
# Optional style modulation factor
style_modifiers = {
"dramatic": 0.9,
"urgent": 1.1,
"neutral": 1.0
}
for idx, entry in enumerate(translated_json):
start, end = float(entry.get("start", 0)), float(entry.get("end", 0))
duration = max(0.1, end - start)
original_text = entry.get("original", "")
translated_text = entry.get("translated", "")
category = entry.get("category", "drama")
source_lang = source_language
target_lang = target_language
style = entry.get("style", "neutral").lower()
# Observed speed from original
base_text = original_text or translated_text
obs_speed = len(base_text) / duration
# Prior speed
prior_speed = priors.get((category, target_lang), default_prior_speed)
# Shrinkage
shrink_speed = (duration * obs_speed + k * prior_speed) / (duration + k)
# Language pacing adjustment
ratio = lang_ratio.get((source_lang, target_lang), 1.0)
adjusted_speed = shrink_speed * ratio
# Style modulation
mod = style_modifiers.get(style, 1.0)
adjusted_speed *= mod
# Final relative speed (normalized to prior)
relative_speed = adjusted_speed / prior_speed
# Clamp relative speed to [0.85, 1.7]
relative_speed = max(0.85, min(1.7, relative_speed))
# Compute target duration for synthesis
target_chars = len(translated_text)
target_duration = round(target_chars / adjusted_speed, 2)
# Logging
logger.info(
f"[Segment {idx}] dur={duration:.2f}s | obs_speed={obs_speed:.2f} | prior={prior_speed:.2f} | "
f"shrinked={shrink_speed:.2f} | lang_ratio={ratio} | style_mod={mod} | "
f"adj_speed={adjusted_speed:.2f} | rel_speed={relative_speed:.2f} | "
f"target_dur={target_duration:.2f}s"
)
entry["speed"] = round(relative_speed, 3)
entry["target_duration"] = target_duration
return translated_json
def calibrated_speed(text, desired_duration):
"""
Compute a speed factor to help TTS fit audio into desired duration,
using a simple truncated linear function of characters per second.
"""
char_count = len(text.strip())
if char_count == 0 or desired_duration <= 0:
return 1.0 # fallback
cps = char_count / desired_duration # characters per second
# Truncated linear mapping
if cps < 14:
return 1.0
elif cps > 25.2:
return 1.7
else:
slope = (1.7 - 1.0) / (25.2 - 14)
return 1.0 + slope * (cps - 14)
def upload_and_manage(file, target_language, process_mode):
if file is None:
logger.info("No file uploaded. Please upload a video/audio file.")
return None, [], None, "No file uploaded. Please upload a video/audio file."
try:
start_time = time.time() # Start the timer
logger.info(f"Started processing file: {file.name}")
# Define paths for audio and output files
audio_path = "audio.wav"
output_video_path = "output_video.mp4"
voiceover_path = "voiceover.wav"
logger.info(f"Using audio path: {audio_path}, output video path: {output_video_path}, voiceover path: {voiceover_path}")
# Step 1: Transcribe audio from uploaded media file and get timestamps
logger.info("Transcribing audio...")
transcription_json, source_language = transcribe_video_with_speakers(file.name)
logger.info(f"Transcription completed. Detected source language: {source_language}")
# Step 2: Translate the transcription
logger.info(f"Translating transcription from {source_language} to {target_language}...")
translated_json_raw = translate_text(transcription_json, source_language, target_language)
logger.info(f"Translation completed. Number of translated segments: {len(translated_json_raw)}")
# translated_json = post_edit_translated_segments(translated_json, file.name)
translated_json = apply_adaptive_speed(translated_json_raw, source_language, target_language)
# Step 3: Add transcript to video based on timestamps
logger.info("Adding translated transcript to video...")
add_transcript_voiceover(file.name, translated_json, output_video_path, process_mode, target_language)
logger.info(f"Transcript added to video. Output video saved at {output_video_path}")
# Convert translated JSON into a format for the editable table
logger.info("Converting translated JSON into editable table format...")
editable_table = [
[float(entry["start"]), entry["original"], entry["translated"], float(entry["end"]), entry["speaker"]]
for entry in translated_json
]
# Calculate elapsed time
elapsed_time = time.time() - start_time
elapsed_time_display = f"Processing completed in {elapsed_time:.2f} seconds."
logger.info(f"Processing completed in {elapsed_time:.2f} seconds.")
return editable_table, output_video_path, elapsed_time_display
except Exception as e:
logger.error(f"An error occurred: {str(e)}")
return [], None, f"An error occurred: {str(e)}"
# Gradio Interface with Tabs
def build_interface():
with gr.Blocks(css=css) as demo:
gr.Markdown("## Video Localization")
with gr.Row():
with gr.Column(scale=4):
file_input = gr.File(label="Upload Video/Audio File")
language_input = gr.Dropdown(["en", "es", "fr", "zh", "de", "it", "pt", "ja", "ko", "ru", "hi", "tr"], label="Select Language") # Language codes
process_mode = gr.Radio(choices=[("Transcription Only", 1),("Transcription with Premium Voice",2),("Transcription with Voice Clone", 3)],label="Choose Processing Type",value=1)
submit_button = gr.Button("Post and Process")
with gr.Column(scale=8):
gr.Markdown("## Edit Translations")
# Editable JSON Data
editable_table = gr.Dataframe(
value=[], # Default to an empty list to avoid undefined values
headers=["start", "original", "translated", "end", "speaker"],
datatype=["number", "str", "str", "number", "str"],
row_count=1, # Initially empty
col_count=5,
interactive=[False, True, True, False, False], # Control editability
label="Edit Translations",
wrap=True # Enables text wrapping if supported
)
save_changes_button = gr.Button("Save Changes")
processed_video_output = gr.File(label="Download Processed Video", interactive=True) # Download button
elapsed_time_display = gr.Textbox(label="Elapsed Time", lines=1, interactive=False)
with gr.Column(scale=1):
gr.Markdown("**Feedback**")
feedback_input = gr.Textbox(
placeholder="Leave your feedback here...",
label=None,
lines=3,
)
feedback_btn = gr.Button("Submit Feedback")
response_message = gr.Textbox(label=None, lines=1, interactive=False)
db_download = gr.File(label="Download Database File", visible=False)
# Link the feedback handling
def feedback_submission(feedback):
message, file_path = handle_feedback(feedback)
if file_path:
return message, gr.update(value=file_path, visible=True)
return message, gr.update(visible=False)
save_changes_button.click(
update_translations,
inputs=[file_input, editable_table, process_mode],
outputs=[processed_video_output, elapsed_time_display]
)
submit_button.click(
upload_and_manage,
inputs=[file_input, language_input, process_mode],
outputs=[editable_table, processed_video_output, elapsed_time_display]
)
# Connect submit button to save_feedback_db function
feedback_btn.click(
feedback_submission,
inputs=[feedback_input],
outputs=[response_message, db_download]
)
return demo
tts_model = None
# Launch the Gradio interface
demo = build_interface()
demo.launch()