app gemini test 1py

import gradio as gr
import librosa
import soundfile
import tempfile
import os
import uuid
import json
import re
import subprocess

from nemo.collections.asr.models import ASRModel
from nemo.utils import logging

from align import main, AlignmentConfig, ASSFileConfig


SAMPLE_RATE = 16000

logging.setLevel(logging.INFO)

# --- ASS TO SRT/LRC/ELRC CONVERTER FUNCTIONS ---

def format_srt_time(secs):
    h = int(secs // 3600)
    m = int((secs % 3600) // 60)
    s = int(secs % 60)
    ms = int(round((secs % 1) * 1000))
    if ms == 1000:
        ms = 0
        s += 1
    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"

def format_lrc_time(secs):
    m = int(secs // 60)
    s = int(secs % 60)
    cs = int(round((secs % 1) * 100))
    if cs == 100:
        cs = 0
        s += 1
    return f"{m:02d}:{s:02d}.{cs:02d}"

def build_color_map(raw_text):
    color_map = []
    current_color = "default"
    regex = re.compile(r'(\{[^}]+\})|([^{]+)')
    for match in regex.finditer(raw_text):
        tag = match.group(1)
        text = match.group(2)
        if tag:
            c_match = re.search(r'\\c&H([0-9a-fA-F]+)&', tag, re.IGNORECASE)
            if c_match:
                current_color = c_match.group(1).lower()
        elif text:
            for _ in text:
                color_map.append(current_color)
    return color_map

def parse_ass_to_segments(text):
    lines = text.split('\n')
    dialogues = []
    
    for line in lines:
        if line.strip().startswith('Dialogue:'):
            parts = line[10:].split(',')
            start = parse_ass_time(parts[1])
            end = parse_ass_time(parts[2])
            raw_text = ','.join(parts[9:])
            clean_text_raw = re.sub(r'\{[^}]+\}', '', raw_text)
            clean_text = re.sub(r'\\N', ' ', clean_text_raw)
            clean_text = re.sub(r'\s+', ' ', clean_text).strip()
            dialogues.append({
                'start': start, 'end': end, 
                'rawText': raw_text, 
                'cleanTextRaw': clean_text_raw, 
                'cleanText': clean_text
            })

    segments = []
    current_segment = None

    for d in dialogues:
        if not current_segment or current_segment['cleanText'] != d['cleanText']:
            if current_segment:
                segments.append(current_segment)
            current_segment = {
                'cleanText': d['cleanText'],
                'cleanTextRaw': d['cleanTextRaw'],
                'startTime': d['start'],
                'endTime': d['end'],
                'slices': [d]
            }
        else:
            current_segment['slices'].append(d)
            current_segment['endTime'] = d['end']
            
    if current_segment:
        segments.append(current_segment)

    for seg in segments:
        if not seg['slices']:
            continue
        base_color_map = build_color_map(seg['slices'][0]['rawText'])
        if not base_color_map:
            seg['words'] = []
            continue
            
        base_color = base_color_map[-1] 

        words = []
        for match in re.finditer(r'\S+', seg['cleanTextRaw']):
            txt = match.group(0).replace('\\N', '')
            if txt.strip():
                words.append({
                    'text': txt,
                    'startIndex': match.start(),
                    'isFirstOfSegment': len(words) == 0
                })

        for w in words:
            char_pos = w['startIndex']
            c0 = base_color_map[char_pos] if char_pos < len(base_color_map) else "default"
            w_start = seg['startTime']
            w_end = seg['endTime']

            transitions = []
            prev_color = c0

            for i in range(1, len(seg['slices'])):
                slice_d = seg['slices'][i]
                cmap = build_color_map(slice_d['rawText'])
                c_curr = cmap[char_pos] if char_pos < len(cmap) else "default"
                if c_curr != prev_color:
                    transitions.append({'time': slice_d['start'], 'color': c_curr})
                    prev_color = c_curr

            if c0 == base_color:
                w_start = transitions[0]['time'] if len(transitions) > 0 else seg['startTime']
                w_end = transitions[1]['time'] if len(transitions) > 1 else seg['endTime']
            else:
                w_start = seg['startTime']
                w_end = transitions[0]['time'] if len(transitions) > 0 else seg['endTime']

            w['startTime'] = w_start
            w['endTime'] = w_end
            
        if words:
            words[0]['startTime'] = seg['startTime']
            words[-1]['endTime'] = seg['endTime']

        seg['words'] = words

    return segments

def generate_srt_segments(segments):
    out = []
    counter = 1
    for seg in segments:
        out.append(str(counter))
        out.append(f"{format_srt_time(seg['startTime'])} --> {format_srt_time(seg['endTime'])}")
        out.append(seg['cleanText'])
        out.append("")
        counter += 1
    return "\n".join(out).strip()

def generate_srt_word_by_word(segments):
    out = []
    counter = 1
    for seg in segments:
        for w in seg['words']:
            out.append(str(counter))
            out.append(f"{format_srt_time(w['startTime'])} --> {format_srt_time(w['endTime'])}")
            out.append(w['text'])
            out.append("")
            counter += 1
    return "\n".join(out).strip()

def generate_srt_additive(segments):
    out = []
    counter = 1
    for seg in segments:
        accumulated = []
        for i, w in enumerate(seg['words']):
            out.append(str(counter))
            actual_start = w['startTime'] if i == 0 else seg['words'][i-1]['endTime']
            out.append(f"{format_srt_time(actual_start)} --> {format_srt_time(w['endTime'])}")
            accumulated.append(w['text'])
            out.append(" ".join(accumulated))
            out.append("")
            counter += 1
    return "\n".join(out).strip()

def generate_lrc(segments):
    out = []
    for seg in segments:
        out.append(f"[{format_lrc_time(seg['startTime'])}]{seg['cleanText']}")
        out.append(f"[{format_lrc_time(seg['endTime'])}]")
    return "\n".join(out).strip()

def generate_elrc(segments):
    out = []
    for seg in segments:
        line = f"[{format_lrc_time(seg['startTime'])}]"
        for i, w in enumerate(seg['words']):
            if i == 0:
                line += f"<{format_lrc_time(w['startTime'])}>{w['text']}"
            else:
                line += f" <{format_lrc_time(w['startTime'])}>{w['text']}"
        out.append(line)
        out.append(f"[{format_lrc_time(seg['endTime'])}]")
    return "\n".join(out).strip()

# --- ORIGINAL PARSING FUNCTIONS ---

def parse_srt(content):
    pattern = re.compile(r'\d+\n(\d{2}:\d{2}:\d{2},\d{3}) --> (\d{2}:\d{2}:\d{2},\d{3})\n((?:(?!\n\n).)*)', re.DOTALL)
    matches = pattern.findall(content + "\n\n") 
    segments = []
    
    def time_to_sec(t_str):
        h, m, s_ms = t_str.split(':')
        s, ms = s_ms.split(',')
        return int(h) * 3600 + int(m) * 60 + int(s) + int(ms) / 1000.0
        
    for match in matches:
        start = time_to_sec(match[0])
        end = time_to_sec(match[1])
        text = match[2].replace('\n', ' ').strip()
        segments.append({"start": start, "end": end, "text": text})
        
    return segments

def parse_lrc(content, audio_duration):
    lines = content.split('\n')
    pattern = re.compile(r'\[(\d{2}):(\d{2}\.\d{2,3})\](.*)')
    raw_markers = []
    
    for line in lines:
        match = pattern.match(line.strip())
        if match:
            m, s, text = match.groups()
            start = int(m) * 60 + float(s)
            raw_markers.append({"start": start, "text": text.strip()})
            
    segments = []
    for i in range(len(raw_markers)):
        current = raw_markers[i]
        text = current["text"]
        
        if text and text != "#":
            end_time = audio_duration
            if i + 1 < len(raw_markers):
                end_time = raw_markers[i+1]["start"]
                
            if end_time > current["start"]:
                segments.append({
                    "start": current["start"], 
                    "end": end_time, 
                    "text": text
                })
                
    return segments

def parse_ass_time(t_str):
    h, m, s = t_str.strip().split(':')
    return float(h) * 3600 + float(m) * 60 + float(s)

def format_ass_time(sec):
    sec = max(0.0, sec)
    nh = int(sec // 3600)
    nm = int((sec % 3600) // 60)
    ns = sec % 60
    return f"{nh:d}:{nm:02d}:{ns:05.2f}"

# -----------------------------

def get_audio_data_and_duration(file):
    data, sr = librosa.load(file)

    if sr != SAMPLE_RATE:
        data = librosa.resample(data, orig_sr=sr, target_sr=SAMPLE_RATE)

    data = librosa.to_mono(data)
    duration = librosa.get_duration(y=data, sr=SAMPLE_RATE)
    return data, duration


def get_char_tokens(text, model):
    tokens = []
    for character in text:
        if character in model.decoder.vocabulary:
            tokens.append(model.decoder.vocabulary.index(character))
        else:
            tokens.append(len(model.decoder.vocabulary))

    return tokens


def get_S_prime_and_T(text, model_name, model, audio_duration):
    if "citrinet" in model_name or "_fastconformer_" in model_name:
        output_timestep_duration = 0.08
    elif "_conformer_" in model_name:
        output_timestep_duration = 0.04
    elif "quartznet" in model_name:
        output_timestep_duration = 0.02
    else:
        raise RuntimeError("unexpected model name")

    T = int(audio_duration / output_timestep_duration) + 1

    if hasattr(model, 'tokenizer'):
        all_tokens = model.tokenizer.text_to_ids(text)
    elif hasattr(model.decoder, "vocabulary"): 
        all_tokens = get_char_tokens(text, model)
    else:
        raise RuntimeError("cannot obtain tokens from this model")

    n_token_repetitions = 0
    for i_tok in range(1, len(all_tokens)):
        if all_tokens[i_tok] == all_tokens[i_tok - 1]:
            n_token_repetitions += 1

    S_prime = len(all_tokens) + n_token_repetitions

    return S_prime, T


def delete_mp4s_except_given_filepath(filepath):
    files_in_dir = os.listdir()
    mp4_files_in_dir = [x for x in files_in_dir if x.endswith(".mp4")]
    for mp4_file in mp4_files_in_dir:
        if mp4_file != filepath:
            try:
                os.remove(mp4_file)
            except Exception:
                pass


def align(Microphone, File_Upload, Video_Upload, subs_file, text, split_on_newline, progress=gr.Progress()):
    utt_id = uuid.uuid4()
    output_video_filepath = f"{utt_id}.mp4"
    delete_mp4s_except_given_filepath(output_video_filepath)

    output_info = ""
    ass_text = ""

    progress(0, desc="Validating input")

    # Ensure only ONE source is used
    inputs_provided = sum([Microphone is not None, File_Upload is not None, Video_Upload is not None])
    if inputs_provided > 1:
        raise gr.Error("Please use either the microphone, audio file upload, or video upload - not multiple inputs.")
    elif inputs_provided == 0:
        raise gr.Error("You have to use the microphone, upload an audio file, or upload a video.")

    # Process and handle file source
    extracted_audio_path = None
    if Microphone is not None:
        file = Microphone
    elif File_Upload is not None:
        file = File_Upload
    else:
        # Step: Extract audio track from video safely
        progress(0.05, desc="Extracting audio track from video...")
        
        # Handle Gradio's potential return structure for Video components
        vid_path = Video_Upload['video'] if isinstance(Video_Upload, dict) else Video_Upload
        extracted_audio_path = os.path.abspath(f"extracted_{utt_id}.wav")
        
        try:
            subprocess.run([
                "ffmpeg", "-y", "-i", vid_path, 
                "-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1", 
                extracted_audio_path
            ], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        except subprocess.CalledProcessError as e:
            raise gr.Error(f"Error: Could not extract audio from video. FFMPEG output: {e.stderr.decode()}")
            
        if not os.path.exists(extracted_audio_path):
            raise gr.Error("Error: Audio extraction failed silently. Ensure the video has a readable audio track.")

        file = extracted_audio_path

    audio_data, duration = get_audio_data_and_duration(file)

    # Clean up the extracted temporary audio file
    if extracted_audio_path and os.path.exists(extracted_audio_path):
        os.remove(extracted_audio_path)

    progress(0.1, desc="Loading speech recognition model")
    model_name = "ayymen/stt_zgh_fastconformer_ctc_small"
    model = ASRModel.from_pretrained(model_name)

    segments = []
    if subs_file is not None:
        with open(subs_file.name, 'r', encoding='utf-8') as f:
            subs_content = f.read()
        
        if subs_file.name.lower().endswith('.srt'):
            segments = parse_srt(subs_content)
        elif subs_file.name.lower().endswith('.lrc'):
            segments = parse_lrc(subs_content, duration)
        else:
            raise gr.Error("Subtitle file must be an .srt or .lrc file.")
            
    with tempfile.TemporaryDirectory() as tmpdir:
        manifest_path = os.path.join(tmpdir, f"{utt_id}_manifest.json")
        
        if segments:
            progress(0.2, desc="Chunking audio and generating manifest")
            with open(manifest_path, 'w', encoding='utf-8') as fout:
                for i, seg in enumerate(segments):
                    S_prime, T = get_S_prime_and_T(seg['text'], model_name, model, seg['end'] - seg['start'])
                    if S_prime > T:
                        raise gr.Error(f"Segment {i} ('{seg['text']}') has too much text for its audio duration ({seg['end'] - seg['start']:.2f}s).")

                    start_sample = int(seg['start'] * SAMPLE_RATE)
                    end_sample = int(seg['end'] * SAMPLE_RATE)
                    chunk_data = audio_data[start_sample:end_sample]

                    chunk_path = os.path.join(tmpdir, f"{utt_id}_{i:04d}.wav")
                    soundfile.write(chunk_path, chunk_data, SAMPLE_RATE)

                    seg_text = seg['text'].replace('\n', '|') if split_on_newline else seg['text'].replace('\n', ' ')

                    data = {
                        "audio_filepath": chunk_path,
                        "text": seg_text,
                    }
                    fout.write(f"{json.dumps(data)}\n")
                    
            resegment_text_to_fill_space = False

        else:
            audio_path = os.path.join(tmpdir, f'{utt_id}.wav')
            soundfile.write(audio_path, audio_data, SAMPLE_RATE)

            if not text:
                progress(0.2, desc="Transcribing audio")
                text = model.transcribe([audio_path])[0]
                if 'hybrid' in model_name:
                    text = text[0]

                if text == "":
                    raise gr.Error("ERROR: the ASR model did not detect any speech. Please upload audio with speech.")

                output_info += (
                    "You did not enter any input text, so the ASR model's transcription will be used:\n"
                    "--------------------------\n"
                    f"{text}\n"
                    "--------------------------\n"
                    f"You could try pasting the transcription into the text input box, correcting any"
                    " transcription errors, and clicking 'Submit' again."
                )

            if split_on_newline:
                text = "|".join(list(filter(None, text.split("\n"))))
                
            S_prime, T = get_S_prime_and_T(text, model_name, model, duration)
            if S_prime > T:
                raise gr.Error("The number of tokens in the input text is too long compared to the duration of the audio.")

            with open(manifest_path, 'w', encoding='utf-8') as fout:
                data = {
                    "audio_filepath": audio_path,
                    "text": text,
                }
                fout.write(f"{json.dumps(data)}\n")

            resegment_text_to_fill_space = "|" not in text

        alignment_config = AlignmentConfig(
            pretrained_name=model_name,
            manifest_filepath=manifest_path,
            output_dir=f"{tmpdir}/nfa_output/",
            audio_filepath_parts_in_utt_id=1,
            batch_size=1,
            use_local_attention=True,
            additional_segment_grouping_separator="|",
            save_output_file_formats=["ass", "ctm"],
            ass_file_config=ASSFileConfig(
                fontsize=45,
                resegment_text_to_fill_space=resegment_text_to_fill_space,
                max_lines_per_segment=4,
            ),
        )

        progress(0.5, desc="Aligning audio")
        main(alignment_config)
        progress(0.95, desc="Saving generated alignments")

        ass_path = "word_level.ass"
        word_ctm_path = "word_level.ctm"
        segment_ctm_path = "segment_level.ctm"
        
        if segments:
            merged_ass = ""
            header_written = False
            
            for i, seg in enumerate(segments):
                chunk_ass_path = f"{tmpdir}/nfa_output/ass/words/{utt_id}_{i:04d}.ass"
                if os.path.exists(chunk_ass_path):
                    chunk_lines = []
                    
                    with open(chunk_ass_path, "r", encoding='utf-8') as f:
                        for line in f:
                            if line.startswith("Dialogue:"):
                                parts = line.split(",", 9)
                                if len(parts) >= 10:
                                    chunk_lines.append(parts)
                            elif not header_written:
                                merged_ass += line
                    header_written = True
                    
                    if chunk_lines:
                        for j, parts in enumerate(chunk_lines):
                            local_start = parse_ass_time(parts[1])
                            local_end = parse_ass_time(parts[2])
                            
                            global_start = local_start + seg['start']
                            global_end = local_end + seg['start']
                            
                            if j == 0:
                                global_start = seg['start']
                                
                            if j == len(chunk_lines) - 1:
                                global_end = seg['end']
                                
                                if i < len(segments) - 1:
                                    next_start = segments[i+1]['start']
                                    if global_end >= next_start:
                                        global_end = next_start - 0.05
                            
                            if global_start >= global_end:
                                global_start = global_end - 0.01

                            parts[1] = format_ass_time(global_start)
                            parts[2] = format_ass_time(global_end)
                            
                            merged_ass += ",".join(parts)
            
            with open(ass_path, "w", encoding="utf-8") as f:
                f.write(merged_ass)
                
            ass_text = merged_ass

            for ctm_type, out_path in [("words", word_ctm_path), ("segments", segment_ctm_path)]:
                merged_ctm = ""
                for i, seg in enumerate(segments):
                    chunk_ctm_path = f"{tmpdir}/nfa_output/ctm/{ctm_type}/{utt_id}_{i:04d}.ctm"
                    if os.path.exists(chunk_ctm_path):
                        lines = []
                        with open(chunk_ctm_path, "r", encoding='utf-8') as f:
                            for line in f:
                                parts = line.strip().split()
                                if len(parts) >= 5:
                                    lines.append(parts)
                        
                        for j, parts in enumerate(lines):
                            parts[0] = str(utt_id)
                            l_start = float(parts[2])
                            l_dur = float(parts[3])
                            l_end = l_start + l_dur
                            
                            g_start = l_start + seg['start']
                            g_end = l_end + seg['start']
                            
                            if ctm_type == "segments":
                                g_start = seg['start']
                                g_end = seg['end']
                                if i < len(segments) - 1:
                                    next_start = segments[i+1]['start']
                                    if g_end >= next_start:
                                        g_end = next_start - 0.05
                            else:
                                if j == 0:
                                    g_start = seg['start']
                                if j == len(lines) - 1:
                                    g_end = seg['end']
                                    if i < len(segments) - 1:
                                        next_start = segments[i+1]['start']
                                        if g_end >= next_start:
                                            g_end = next_start - 0.05
                                            
                            if g_start >= g_end:
                                g_start = g_end - 0.01
                                
                            parts[2] = f"{g_start:.2f}"
                            parts[3] = f"{(g_end - g_start):.2f}"
                            merged_ctm += " ".join(parts) + "\n"
                
                with open(out_path, "w", encoding="utf-8") as f:
                    f.write(merged_ctm)

        else:
            ass_file_for_video = f"{tmpdir}/nfa_output/ass/words/{utt_id}.ass"
            with open(ass_file_for_video, "r", encoding="utf-8") as f:
                ass_text = f.read()
            with open(ass_path, "w", encoding="utf-8") as f:
                f.write(ass_text)

            with open(f"{tmpdir}/nfa_output/ctm/words/{utt_id}.ctm", "r", encoding="utf-8") as f:
                with open(word_ctm_path, "w", encoding="utf-8") as out_f:
                    out_f.write(f.read())

            with open(f"{tmpdir}/nfa_output/ctm/segments/{utt_id}.ctm", "r", encoding="utf-8") as f:
                with open(segment_ctm_path, "w", encoding="utf-8") as out_f:
                    out_f.write(f.read())
                    

        segments_for_subs = parse_ass_to_segments(ass_text)

        srt_seg_path = "segments.srt"
        with open(srt_seg_path, "w", encoding="utf-8") as f:
            f.write(generate_srt_segments(segments_for_subs))

        srt_word_path = "word_by_word.srt"
        with open(srt_word_path, "w", encoding="utf-8") as f:
            f.write(generate_srt_word_by_word(segments_for_subs))

        srt_add_path = "additive.srt"
        with open(srt_add_path, "w", encoding="utf-8") as f:
            f.write(generate_srt_additive(segments_for_subs))

        lrc_path = "segments.lrc"
        with open(lrc_path, "w", encoding="utf-8") as f:
            f.write(generate_lrc(segments_for_subs))

        elrc_path = "word_level.elrc"
        with open(elrc_path, "w", encoding="utf-8") as f:
            f.write(generate_elrc(segments_for_subs))

        full_audio_path = os.path.join(tmpdir, "full_audio.wav")
        soundfile.write(full_audio_path, audio_data, SAMPLE_RATE)

        # Added string quotes to safeguard against spaces in temp directories
        ffmpeg_command = (
            f'ffmpeg -y -i "{full_audio_path}" '
            '-f lavfi -i color=c=white:s=1280x720:r=50 '
            '-crf 1 -shortest -vcodec libx264 -pix_fmt yuv420p '
            f'-vf "ass=\'{ass_path}\'" '
            f'"{output_video_filepath}"'
        )
        os.system(ffmpeg_command)

    return (
        output_video_filepath, 
        gr.update(value=output_info, visible=True if output_info else False), 
        output_video_filepath, 
        gr.update(value=ass_path, visible=True), 
        gr.update(value=word_ctm_path, visible=True), 
        gr.update(value=segment_ctm_path, visible=True),
        gr.update(value=srt_seg_path, visible=True),
        gr.update(value=srt_word_path, visible=True),
        gr.update(value=srt_add_path, visible=True),
        gr.update(value=lrc_path, visible=True),
        gr.update(value=elrc_path, visible=True)
    )


def delete_non_tmp_video(video_path):
    if video_path:
        if os.path.exists(video_path):
            try:
                os.remove(video_path)
            except Exception:
                pass
    return None


with gr.Blocks(title="NeMo Forced Aligner", theme="huggingface") as demo:
    non_tmp_output_video_filepath = gr.State([])

    with gr.Row():
        with gr.Column():
            gr.Markdown("# NeMo Forced Aligner")
            gr.Markdown(
                "Demo for [NeMo Forced Aligner](https://github.com/NVIDIA/NeMo/tree/main/tools/nemo_forced_aligner) (NFA). "
                "Upload audio/video in Tamazight and (optionally) the text spoken to generate highlighted karaoke subtitles. "
                "**Now supports syncing with pre-timed SRT or LRC files and generates all subtitle formats instantly!**",
            )

    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("## Input")
            mic_in = gr.Audio(sources=["microphone"], type='filepath', label="Microphone input")
            audio_file_in = gr.Audio(sources=["upload"], type='filepath', label="Audio file upload")
            video_file_in = gr.Video(label="Video file upload (Audio will be extracted automatically)")
            
            subs_file_in = gr.File(label="[Optional] Upload an SRT or LRC file to constrain alignment to predefined timestamps", file_types=[".srt", ".lrc"])
            
            ref_text = gr.Textbox(
                label="[Optional] The reference text. Use '|' separators to specify which text will appear together. "
                "Leave this field blank to use an ASR model's transcription as the reference text instead. (Ignored if SRT/LRC is uploaded)"
            )
            split_on_newline = gr.Checkbox(
                True,
                label="Separate text on new lines",
            )

            submit_button = gr.Button("Submit")

        with gr.Column(scale=1):
            gr.Markdown("## Output Video")
            video_out = gr.Video(label="Output Video")
            text_out = gr.Textbox(label="Output Info", visible=False)
            
            gr.Markdown("## Download Subtitle Files")
            with gr.Row():
                ass_file = gr.File(label="ASS (Karaoke)", visible=False)
                word_ctm_file = gr.File(label="CTM (Word-level)", visible=False)
                segment_ctm_file = gr.File(label="CTM (Segment-level)", visible=False)
                
            with gr.Row():
                srt_seg_file = gr.File(label="SRT (Segments)", visible=False)
                srt_word_file = gr.File(label="SRT (Word-by-Word)", visible=False)
                srt_add_file = gr.File(label="SRT (Additive)", visible=False)
                
            with gr.Row():
                lrc_file = gr.File(label="LRC (Segments)", visible=False)
                elrc_file = gr.File(label="ELRC (Word-level)", visible=False)

    with gr.Row():
        gr.HTML(
            "<p style='text-align: center'>"
                "Tutorial: <a href='https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/tools/NeMo_Forced_Aligner_Tutorial.ipynb' target='_blank'>\"How to use NFA?\"</a> 🚀 | "
                "Blog post: <a href='https://nvidia.github.io/NeMo/blogs/2023/2023-08-forced-alignment/' target='_blank'>\"How does forced alignment work?\"</a> 📚 | "
                "NFA <a href='https://github.com/NVIDIA/NeMo/tree/main/tools/nemo_forced_aligner/' target='_blank'>Github page</a> 👩‍💻"
            "</p>"
        )

    submit_button.click(
        fn=align,
        inputs=[mic_in, audio_file_in, video_file_in, subs_file_in, ref_text, split_on_newline],
        outputs=[
            video_out, text_out, non_tmp_output_video_filepath, 
            ass_file, word_ctm_file, segment_ctm_file,
            srt_seg_file, srt_word_file, srt_add_file, lrc_file, elrc_file
        ],
    ).then(
        fn=delete_non_tmp_video, inputs=[non_tmp_output_video_filepath], outputs=None,
    )
    
    example_2 = """ⵜⴰⴽⵟⵟⵓⵎⵜ ⵏ ⵜⵙⴰⴷⵓⴼⵜ.
ⵙ ⵉⵙⵎ ⵏ ⵕⴱⴱⵉ ⴰⵎⴰⵍⵍⴰⵢ ⴰⵎⵙⵎⵓⵍⵍⵓ.
ⴰⵎⵓⵢ ⵉ ⵕⴱⴱⵉ ⵍⵍⵉ ⵎⵓ ⵜⴳⴰ ⵜⵓⵍⵖⵉⵜ ⵜⵉⵏⵏⵙ, ⵕⴱⴱⵉ ⵏ ⵉⵖⵥⵡⴰⵕⵏ, ⴽⵔⴰ ⴳⴰⵏ.
ⴰⵎⴰⵍⵍⴰⵢ ⴰⵎⵙⵎⵓⵍⵍⵓ, ⵖ ⵜⵎⵣⵡⴰⵔⵓⵜ ⵓⵍⴰ ⵖ ⵜⵎⴳⴳⴰⵔⵓⵜ.
ⴰⴳⵍⵍⵉⴷ ⵏ ⵡⴰⵙⵙ ⵏ ⵓⴼⵔⴰ, ⴰⵙⵙ ⵏ ⵓⵙⵙⵃⵙⵓ, ⴽⵔⴰⵉⴳⴰⵜ ⵢⴰⵏ ⴷ ⵎⴰⴷ ⵉⵙⴽⵔ.
ⵀⴰ ⵏⵏ ⴽⵢⵢⵉ ⴽⴰ ⵙ ⵏⵙⵙⵓⵎⴷ, ⴷ ⴽⵢⵢⵉ ⴽⴰ ⴰⴷ ⵏⵎⵎⵜⵔ.
ⵙⵎⵓⵏ ⴰⵖ, ⵜⵎⵍⵜ ⴰⵖ, ⴰⵖⴰⵔⴰⵙ ⵢⵓⵖⴷⵏ.
ⴰⵖⴰⵔⴰⵙ ⵏ ⵖⵡⵉⵍⵍⵉ ⵜⵙⵏⵏⵓⴼⴰⵜ, ⵓⵔ ⴷ ⴰⵢⵜ ⵜⵉⵢⵓⵔⵉ, ⵓⵍⴰ ⵉⵎⵓⴹⴹⴰⵕ."""

    examples = gr.Examples(
        examples=[
            ["Voice1410.wav", None, None, example_2],
            ["Tamazight_For_All.mp3", None, "Tamazight_For_All.srt", ""]
        ],
        inputs=[audio_file_in, video_file_in, subs_file_in, ref_text]
    )
    
    demo.queue()
    demo.launch()