app.py

import spaces
import boto3
from botocore.exceptions import NoCredentialsError, ClientError
from botocore.client import Config

import os, pathlib

CACHE_ROOT        = "/home/user/app/cache"          # any folder you own
os.environ.update(
    TORCH_HOME        = f"{CACHE_ROOT}/torch",
    XDG_CACHE_HOME    = f"{CACHE_ROOT}/xdg",        # torch fallback
    PYANNOTE_CACHE    = f"{CACHE_ROOT}/pyannote",
    HF_HOME           = f"{CACHE_ROOT}/huggingface",
    TRANSFORMERS_CACHE= f"{CACHE_ROOT}/transformers",
    MPLCONFIGDIR      = f"{CACHE_ROOT}/mpl",
)

INITIAL_PROMPT = '''
Use normal punctuation; end sentences properly.
'''

# make sure the directories exist
for path in os.environ.values():
    pathlib.Path(path).mkdir(parents=True, exist_ok=True)

import gradio as gr
import torch
import torchaudio
import numpy as np
import pandas as pd
import time
import datetime
import re
import subprocess
import os
import tempfile
import spaces
from faster_whisper import WhisperModel, BatchedInferencePipeline
from faster_whisper.vad import VadOptions
import requests
import base64
from pyannote.audio import Pipeline, Inference, Model
from pyannote.core import Segment

import os, sys, importlib.util, pathlib, ctypes, tempfile, wave, math
import json
import webrtcvad
spec = importlib.util.find_spec("nvidia.cudnn")
if spec is None:
    sys.exit("❌  nvidia-cudnn-cu12 wheel not found.  Run:  pip install nvidia-cudnn-cu12")

cudnn_dir = pathlib.Path(spec.origin).parent / "lib"
cnn_so     = cudnn_dir / "libcudnn_cnn.so.9"

try:
    ctypes.CDLL(cnn_so, mode=ctypes.RTLD_GLOBAL)
    print(f"✓  Pre-loaded {cnn_so}")
except OSError as e:
    sys.exit(f"❌  Could not load {cnn_so} : {e}")

S3_ENDPOINT = os.getenv("S3_ENDPOINT")
S3_ACCESS_KEY = os.getenv("S3_ACCESS_KEY")
S3_SECRET_KEY = os.getenv("S3_SECRET_KEY")



# Function to upload file to Cloudflare R2
def upload_data_to_r2(data, bucket_name, object_name, content_type='application/octet-stream'):
    """
    Upload data directly to a Cloudflare R2 bucket.

    :param data: Data to upload (bytes or string).
    :param bucket_name: Name of the R2 bucket.
    :param object_name: Name of the object to save in the bucket.
    :param content_type: MIME type of the data.
    :return: True if data was uploaded, else False.
    """
    try:
        # Convert string to bytes if necessary
        if isinstance(data, str):
            data = data.encode('utf-8')
        
        # Initialize a session using Cloudflare R2 credentials
        session = boto3.session.Session()
        s3 = session.client('s3',
            endpoint_url=f'https://{S3_ENDPOINT}',
            aws_access_key_id=S3_ACCESS_KEY,
            aws_secret_access_key=S3_SECRET_KEY,
            config = Config(s3={"addressing_style": "virtual", 'payload_signing_enabled': False}, signature_version='v4',
                request_checksum_calculation='when_required',
                response_checksum_validation='when_required',),
        )

        # Upload the data to R2 bucket
        s3.put_object(
            Bucket=bucket_name, 
            Key=object_name, 
            Body=data,
            ContentType=content_type,
            ContentLength=len(data),  # make length explicit to avoid streaming
        )
        print(f"Data uploaded to R2 bucket '{bucket_name}' as '{object_name}'")
        return True
    except NoCredentialsError:
        print("Credentials not available")
        return False
    except ClientError as e:
        print(f"Failed to upload data to R2 bucket: {e}")
        return False
    except Exception as e:
        print(f"An unexpected error occurred: {e}")
        return False
    
from huggingface_hub import snapshot_download
MODEL_REPO = "deepdml/faster-whisper-large-v3-turbo-ct2"   # CT2 format
LOCAL_DIR  = f"{CACHE_ROOT}/whisper_turbo"

# -----------------------------------------------------------------------------
# Audio preprocess helper (from input_and_preprocess rule)
# -----------------------------------------------------------------------------

TRIM_THRESHOLD_MS = 10_000  # 10 seconds
DEFAULT_PAD_MS    = 250     # safety context around detected speech
FRAME_MS          = 30      # VAD frame
HANG_MS           = 240     # hangover (keep speech "on" after silence)
VAD_LEVEL         = 2       # 0-3

def _decode_chunk_to_pcm(task: dict) -> bytes:
    """Use ffmpeg to decode the chunk to s16le mono @ 16k PCM bytes."""
    src = task["source_uri"]
    ing = task["ingest_recipe"]
    seek = task["ffmpeg_seek"]

    cmd = [
        "ffmpeg", "-nostdin", "-hide_banner", "-v", "error",
        "-ss", f"{max(0.0, float(seek['pre_ss_sec'])):.3f}",
        "-i", src,
        "-map", "0:a:0",
        "-ss", f"{float(seek['post_ss_sec']):.2f}",
        "-t", f"{float(seek['t_sec']):.3f}",
    ]

    # Optional L/R extraction
    if ing.get("channel_extract_filter"):
        cmd += ["-af", ing["channel_extract_filter"]]

    # Force mono 16k s16le to stdout
    cmd += ["-ar", "16000", "-ac", "1", "-c:a", "pcm_s16le", "-f", "s16le", "pipe:1"]

    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    pcm, err = p.communicate()
    if p.returncode != 0:
        raise RuntimeError(f"ffmpeg failed: {err.decode('utf-8', 'ignore')}")
    return pcm

def _find_head_tail_speech_ms(
    pcm: bytes,
    sr: int = 16000,
    frame_ms: int = FRAME_MS,
    vad_level: int = VAD_LEVEL,
    hang_ms: int = HANG_MS,
):
    """Return (first_ms, last_ms) speech boundaries using webrtcvad with hangover."""
    if not pcm:
        return None, None
    vad = webrtcvad.Vad(int(vad_level))
    bpf = 2  # bytes per sample (s16)
    samples_per_ms = sr // 1000  # 16
    bytes_per_frame = samples_per_ms * bpf * frame_ms

    n_frames = len(pcm) // bytes_per_frame
    if n_frames == 0:
        return None, None

    first_ms, last_ms = None, None
    t_ms = 0
    in_speech = False
    silence_run = 0

    view = memoryview(pcm)[: n_frames * bytes_per_frame]
    for i in range(n_frames):
        frame = view[i * bytes_per_frame : (i + 1) * bytes_per_frame]
        if vad.is_speech(frame, sr):
            if first_ms is None:
                first_ms = t_ms
            in_speech = True
            silence_run = 0
        else:
            if in_speech:
                silence_run += frame_ms
                if silence_run >= hang_ms:
                    last_ms = t_ms - (silence_run - hang_ms)
                    in_speech = False
                    silence_run = 0
        t_ms += frame_ms
    if in_speech:
        last_ms = t_ms
    return first_ms, last_ms

def _write_wav(path: str, pcm: bytes, sr: int = 16000):
    os.makedirs(os.path.dirname(path), exist_ok=True)
    with wave.open(path, "wb") as w:
        w.setnchannels(1)
        w.setsampwidth(2)  # s16
        w.setframerate(sr)
        w.writeframes(pcm)

def prepare_and_save_audio_for_model(task: dict, out_dir: str) -> dict:
    """
    1) Decode chunk to mono 16k PCM.
    2) Run VAD to locate head/tail silence.
    3) Trim only if head or tail >= 10s.
    4) Save the (possibly trimmed) WAV to local file.
    5) Return timing metadata, including 'trimmed_start_ms' to preserve global timestamps.
    """
    # 0) Names & constants
    sr = 16000
    bpf = 2
    samples_per_ms = sr // 1000

    def bytes_from_ms(ms: int) -> int:
        return int(ms * samples_per_ms) * bpf

    ch = task["channel"]
    ck = task["chunk"]
    job = task.get("job_id", "job")
    idx = str(ck["idx"])

    # 1) Decode chunk
    pcm = _decode_chunk_to_pcm(task)
    planned_dur_ms = int(ck["dur_ms"])

    # 2) VAD head/tail detection
    first_ms, last_ms = _find_head_tail_speech_ms(pcm, sr=sr)
    head_sil_ms = int(first_ms) if first_ms is not None else planned_dur_ms
    tail_sil_ms = int(planned_dur_ms - last_ms) if last_ms is not None else planned_dur_ms

    # 3) Decide trimming (only if head or tail >= 10s)
    trim_applied = False
    eff_start_ms = 0
    eff_end_ms = planned_dur_ms
    trimmed_pcm = pcm

    if (head_sil_ms >= TRIM_THRESHOLD_MS) or (tail_sil_ms >= TRIM_THRESHOLD_MS):
        # If no speech found at all, mark skip
        if first_ms is None or last_ms is None or last_ms <= first_ms:
            out_wav_path = os.path.join(out_dir, f"{job}_{ch}_{idx}_nospeech.wav")
            _write_wav(out_wav_path, b"", sr)
            return {
                "out_wav_path": out_wav_path,
                "sr": sr,
                "trim_applied": False,
                "trimmed_start_ms": 0,
                "head_silence_ms": head_sil_ms,
                "tail_silence_ms": tail_sil_ms,
                "effective_start_ms": 0,
                "effective_dur_ms": 0,
                "abs_start_ms": ck["global_offset_ms"],
                "chunk_idx": idx,
                "channel": ch,
                "skip": True,
            }

        # Apply padding & slice
        start_ms = max(0, int(first_ms) - DEFAULT_PAD_MS)
        end_ms = min(planned_dur_ms, int(last_ms) + DEFAULT_PAD_MS)

        if end_ms > start_ms:
            eff_start_ms = start_ms
            eff_end_ms = end_ms
            trimmed_pcm = pcm[bytes_from_ms(start_ms) : bytes_from_ms(end_ms)]
            trim_applied = True

    # 4) Write WAV to local file (trimmed or original)
    tag = "trim" if trim_applied else "full"
    out_wav_path = os.path.join(out_dir, f"{job}_{ch}_{idx}_{tag}.wav")
    _write_wav(out_wav_path, trimmed_pcm, sr)

    # 5) Return metadata
    return {
        "out_wav_path": out_wav_path,
        "sr": sr,
        "trim_applied": trim_applied,
        "trimmed_start_ms": eff_start_ms if trim_applied else 0,
        "head_silence_ms": head_sil_ms,
        "tail_silence_ms": tail_sil_ms,
        "effective_start_ms": eff_start_ms,
        "effective_dur_ms": eff_end_ms - eff_start_ms,
        "abs_start_ms": int(ck["global_offset_ms"]) + eff_start_ms,
        "chunk_idx": idx,
        "channel": ch,
        "job_id": job,
        "skip": False if (trim_applied or len(pcm) > 0) else True,
    }

# Download once; later runs are instant
snapshot_download(
    repo_id=MODEL_REPO,
    local_dir=LOCAL_DIR,
    local_dir_use_symlinks=True,   # saves disk space
    resume_download=True
)
model_cache_path = LOCAL_DIR      # <‑‑ this is what we pass to WhisperModel

# Lazy global holder ----------------------------------------------------------
_whisper = None
_batched_whisper = None
_diarizer = None
_embedder = None

# Create global diarization pipeline
try:
    print("Loading diarization model...")
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.backends.cudnn.allow_tf32 = True
    torch.set_float32_matmul_precision('high')

    _diarizer = Pipeline.from_pretrained(
        "pyannote/speaker-diarization-3.1",
        use_auth_token=os.getenv("HF_TOKEN"),
    ).to(torch.device("cuda"))
    
    print("Diarization model loaded successfully")
except Exception as e:
    import traceback
    traceback.print_exc()
    print(f"Could not load diarization model: {e}")
    _diarizer = None

@spaces.GPU   # GPU is guaranteed to exist *inside* this function
def _load_models():
    global _whisper, _batched_whisper, _diarizer
    if _whisper is None:
        print("Loading Whisper model...")
        _whisper = WhisperModel(
            model_cache_path,
            device="cuda",
            compute_type="float16",
        )
        
        # Create batched inference pipeline for improved performance
        _batched_whisper = BatchedInferencePipeline(model=_whisper)
        print("Whisper model and batched pipeline loaded successfully")
    return _whisper, _batched_whisper, _diarizer

# -----------------------------------------------------------------------------    
class WhisperTranscriber:
    def __init__(self):
        # do **not** create the models here!
        pass
        
    def preprocess_from_task_json(self, task_json: str) -> dict:
        """Parse task JSON and run prepare_and_save_audio_for_model, returning metadata."""
        try:
            task = json.loads(task_json)
        except Exception as e:
            raise RuntimeError(f"Invalid JSON: {e}")

        out_dir = os.path.join(CACHE_ROOT, "preprocessed")
        os.makedirs(out_dir, exist_ok=True)
        meta = prepare_and_save_audio_for_model(task, out_dir)
        return meta
    
    @spaces.GPU           # each call gets a GPU slice
    def transcribe_full_audio(self, audio_path, language=None, translate=False, prompt=None, batch_size=16, base_offset_s: float = 0.0):
        """Transcribe the entire audio file without speaker diarization using batched inference"""
        whisper, batched_whisper, _ = _load_models()   # models live on the GPU
        
        print(f"Transcribing full audio with batch size {batch_size}...")
        start_time = time.time()
        
        # Prepare options for batched inference
        options = dict(
            language=language,
            beam_size=5,
            vad_filter=True,  # VAD is enabled by default for batched transcription
            vad_parameters=VadOptions(
                max_speech_duration_s=whisper.feature_extractor.chunk_length,
                min_speech_duration_ms=150,   # ignore ultra-short blips
                min_silence_duration_ms=150,  # split on short Mandarin pauses (if supported)                speech_pad_ms=100,
                threshold=0.25,
                neg_threshold=0.2,

            ),
            word_timestamps=True,
            initial_prompt=prompt,
            condition_on_previous_text=False,  # avoid runaway context
            language_detection_segments=1,
            task="translate" if translate else "transcribe",
        )
        if batch_size > 1:
            # Use batched inference for better performance
            segments, transcript_info = batched_whisper.transcribe(
                audio_path, 
                batch_size=batch_size, 
                **options
            )
        else:
            segments, transcript_info = whisper.transcribe(
                audio_path, 
                **options
            )
        segments = list(segments)
        
        detected_language = transcript_info.language
        print("Detected language: ", detected_language, "segments: ", len(segments))
        
        # Process segments
        results = []
        for seg in segments:
            # Create result entry with detailed format
            words_list = []
            if seg.words:
                for word in seg.words:
                    words_list.append({
                        "start": float(word.start) + float(base_offset_s),
                        "end": float(word.end) + float(base_offset_s),
                        "word": word.word,
                        "probability": word.probability,
                        "speaker": "SPEAKER_00"  # No speaker identification in full transcription
                    })
            
            results.append({
                "start": float(seg.start) + float(base_offset_s),
                "end": float(seg.end) + float(base_offset_s),
                "text": seg.text,
                "speaker": "SPEAKER_00",  # Single speaker assumption
                "avg_logprob": seg.avg_logprob,
                "words": words_list,
                "duration": float(seg.end - seg.start)
            })
        
        transcription_time = time.time() - start_time
        print(f"Full audio transcribed in {transcription_time:.2f} seconds using batch size {batch_size}")
        #print(results)
        return results, detected_language
    
    # Removed audio cutting; transcription is done once on the full (preprocessed) audio
    
    @spaces.GPU           # each call gets a GPU slice
    # Removed segment-wise transcription; using single full-audio transcription
    
    @spaces.GPU           # each call gets a GPU slice
    def perform_diarization(self, audio_path, num_speakers=None, base_offset_s: float = 0.0):
        """Perform speaker diarization; return segments with global timestamps and per-speaker embeddings."""
        _, _, diarizer = _load_models()   # models live on the GPU
        
        if diarizer is None:
            print("Diarization model not available, creating single speaker segment")
            # Load audio to get duration
            waveform, sample_rate = torchaudio.load(audio_path)
            duration = waveform.shape[1] / sample_rate
            # Try to compute a single-speaker embedding
            speaker_embeddings = {}
            try:
                embedder = self._load_embedder()
                # Provide waveform as (channel, time) and pad if too short
                min_embed_duration_sec = 3.0
                min_samples = int(min_embed_duration_sec * sample_rate)
                if waveform.shape[1] < min_samples:
                    pad_len = min_samples - waveform.shape[1]
                    pad = torch.zeros(waveform.shape[0], pad_len, dtype=waveform.dtype, device=waveform.device)
                    waveform = torch.cat([waveform, pad], dim=1)
                emb = embedder({"waveform": waveform, "sample_rate": sample_rate})
                speaker_embeddings["SPEAKER_00"] = emb.squeeze().tolist()
            except Exception:
                pass
            return [{
                "start": 0.0 + float(base_offset_s),
                "end": duration + float(base_offset_s),
                "speaker": "SPEAKER_00"
            }], 1, speaker_embeddings
            
        print("Starting diarization...")
        start_time = time.time()
        
        # Load audio for diarization
        waveform, sample_rate = torchaudio.load(audio_path)
        
        # Perform diarization
        diarization = diarizer(
            {"waveform": waveform, "sample_rate": sample_rate},
            num_speakers=num_speakers,
        )
        
        # Convert to list format
        diarize_segments = []
        diarization_list = list(diarization.itertracks(yield_label=True))
        #print(diarization_list)
        for turn, _, speaker in diarization_list:
            diarize_segments.append({
                "start": float(turn.start) + float(base_offset_s),
                "end": float(turn.end) + float(base_offset_s),
                "speaker": speaker
            })
            
        unique_speakers = {speaker for segment in diarize_segments for speaker in [segment["speaker"]]}
        detected_num_speakers = len(unique_speakers)
        
        # Compute per-speaker embeddings by averaging segment embeddings
        speaker_embeddings = {}
        try:
            embedder = self._load_embedder()
            spk_to_embs = {spk: [] for spk in unique_speakers}
            # Primary path: slice in-memory waveform and zero-pad short segments
            min_embed_duration_sec = 3.0
            audio_duration_sec = float(waveform.shape[1]) / float(sample_rate)
            for turn, _, speaker in diarization_list:
                seg_start = float(turn.start)
                seg_end = float(turn.end)
                if seg_end <= seg_start:
                    continue
                start_sample = max(0, int(seg_start * sample_rate))
                end_sample = min(waveform.shape[1], int(seg_end * sample_rate))
                if end_sample <= start_sample:
                    continue
                seg_wav = waveform[:, start_sample:end_sample].contiguous()
                min_samples = int(min_embed_duration_sec * sample_rate)
                if seg_wav.shape[1] < min_samples:
                    pad_len = min_samples - seg_wav.shape[1]
                    pad = torch.zeros(seg_wav.shape[0], pad_len, dtype=seg_wav.dtype, device=seg_wav.device)
                    seg_wav = torch.cat([seg_wav, pad], dim=1)
                try:
                    emb = embedder({"waveform": seg_wav, "sample_rate": sample_rate})
                except Exception:
                    # Fallback: use crop on the file with expanded window to minimum duration
                    desired_end = min(seg_start + min_embed_duration_sec, audio_duration_sec)
                    desired_start = max(0.0, desired_end - min_embed_duration_sec)
                    emb = embedder.crop(audio_path, Segment(desired_start, desired_end))
                spk_to_embs[speaker].append(emb.squeeze())
            # average
            for spk, embs in spk_to_embs.items():
                if len(embs) == 0:
                    continue
                # stack and mean
                try:
                    import torch as _torch
                    embs_tensor = _torch.stack([_torch.as_tensor(e) for e in embs], dim=0)
                    centroid = embs_tensor.mean(dim=0)
                    # L2 normalize
                    centroid = centroid / (centroid.norm(p=2) + 1e-12)
                    speaker_embeddings[spk] = centroid.cpu().tolist()
                except Exception:
                    # fallback to first embedding
                    speaker_embeddings[spk] = embs[0].cpu().tolist()
                #print(speaker_embeddings[spk])
        except Exception as e:
            print(f"Error during embedding calculation: {e}")
            print(f"Diarization segments: {diarize_segments}")
            pass
        
        diarization_time = time.time() - start_time
        print(f"Diarization completed in {diarization_time:.2f} seconds")
        
        return diarize_segments, detected_num_speakers, speaker_embeddings

    def _load_embedder(self):
        """Lazy-load speaker embedding inference model on GPU."""
        global _embedder
        if _embedder is None:
            # window="whole" to compute one embedding per provided chunk
            token = os.getenv("HF_TOKEN")
            model = Model.from_pretrained("pyannote/embedding", use_auth_token=token)
            _embedder = Inference(model, window="whole", device=torch.device("cuda"))
        return _embedder

    def assign_speakers_to_transcription(self, transcription_results, diarization_segments):
        """Assign speakers to words and segments based on overlap with diarization segments."""
        if not diarization_segments:
            return transcription_results
        # simple helper to find speaker at given time
        def speaker_at(t: float):
            for seg in diarization_segments:
                if seg["start"] <= t < seg["end"]:
                    return seg["speaker"]
            # if not inside, return closest segment's speaker
            closest = None
            best = float("inf")
            for seg in diarization_segments:
                if t < seg["start"]:
                    d = seg["start"] - t
                elif t > seg["end"]:
                    d = t - seg["end"]
                else:
                    d = 0.0
                if d < best:
                    best = d
                    closest = seg
            return closest["speaker"] if closest else "SPEAKER_00"

        for seg in transcription_results:
            # Assign per-word speakers
            if seg.get("words"):
                speaker_counts = {}
                for w in seg["words"]:
                    mid = (float(w["start"]) + float(w["end"])) / 2.0
                    spk = speaker_at(mid)
                    w["speaker"] = spk
                    speaker_counts[spk] = speaker_counts.get(spk, 0) + (float(w["end"]) - float(w["start"]))
                # Segment speaker = speaker with max accumulated word duration
                if speaker_counts:
                    seg["speaker"] = max(speaker_counts.items(), key=lambda kv: kv[1])[0]
            else:
                mid = (float(seg["start"]) + float(seg["end"])) / 2.0
                seg["speaker"] = speaker_at(mid)
        return transcription_results
    
    def group_segments_by_speaker(self, segments, max_gap=1.0, max_duration=30.0):
        """Group consecutive segments from the same speaker"""
        if not segments:
            return segments
            
        grouped_segments = []
        current_group = segments[0].copy()
        sentence_end_pattern = r"[.!?]+"
        
        for segment in segments[1:]:
            time_gap = segment["start"] - current_group["end"]
            current_duration = current_group["end"] - current_group["start"]
            
            # Conditions for combining segments
            can_combine = (
                segment["speaker"] == current_group["speaker"] and
                time_gap <= max_gap and
                current_duration < max_duration and
                not re.search(sentence_end_pattern, current_group["text"][-1:])
            )
            
            if can_combine:
                # Merge segments
                current_group["end"] = segment["end"]
                current_group["text"] += " " + segment["text"]
                current_group["words"].extend(segment["words"])
                current_group["duration"] = current_group["end"] - current_group["start"]
            else:
                # Start new group
                grouped_segments.append(current_group)
                current_group = segment.copy()
                
        grouped_segments.append(current_group)
        
        # Clean up text
        for segment in grouped_segments:
            segment["text"] = re.sub(r"\s+", " ", segment["text"]).strip()
            segment["text"] = re.sub(r"\s+([.,!?])", r"\1", segment["text"])
            
        return grouped_segments
    
    @spaces.GPU           # each call gets a GPU slice
    def process_audio_full(self, task_json, language=None, translate=False, prompt=None, group_segments=True, batch_size=16):
        """Process a single chunk using task JSON (no diarization)."""
        if not task_json or not str(task_json).strip():
            return {"error": "No JSON provided"}
        
        pre_meta = None
        try:
            print("Starting full transcription pipeline...")
            
            # Step 1: Preprocess per chunk JSON
            print("Preprocessing chunk JSON...")
            pre_meta = self.preprocess_from_task_json(task_json)
            if pre_meta.get("skip"):
                return {"segments": [], "language": "unknown", "num_speakers": 1, "transcription_method": "full_audio_batched", "batch_size": batch_size}
            wav_path = pre_meta["out_wav_path"]
            # Adjust timestamps by trimmed_start_ms: abs_start_ms is already global start for saved file
            base_offset_s = float(pre_meta.get("abs_start_ms", 0)) / 1000.0
            
            # Step 2: Transcribe the entire audio with batching
            transcription_results, detected_language = self.transcribe_full_audio(
                wav_path, language, translate, prompt, batch_size, base_offset_s=base_offset_s
            )
            
            # Step 3: Group segments if requested (based on time gaps and sentence endings)
            if group_segments:
                transcription_results = self.group_segments_by_speaker(transcription_results)
            
            # Step 4: Return results
            return {
                "segments": transcription_results,
                "language": detected_language,
                "num_speakers": 1,  # Single speaker assumption
                "transcription_method": "full_audio_batched",
                "batch_size": batch_size
            }
                
        except Exception as e:
            import traceback
            traceback.print_exc()
            return {"error": f"Processing failed: {str(e)}"}
        finally:
            # Clean up preprocessed wav
            if pre_meta and pre_meta.get("out_wav_path") and os.path.exists(pre_meta["out_wav_path"]):
                try:
                    os.unlink(pre_meta["out_wav_path"])
                except Exception:
                    pass
    
    @spaces.GPU           # each call gets a GPU slice
    def process_audio(self, task_json, num_speakers=None, language=None, 
                     translate=False, prompt=None, group_segments=True, batch_size=8):
        """Main processing function with diarization using task JSON for a single chunk.

        Transcribes full (preprocessed) audio once, performs diarization, merges speakers into transcription.
        """
        if not task_json or not str(task_json).strip():
            return {"error": "No JSON provided"}
        
        pre_meta = None
        try:
            print("Starting new processing pipeline...")
            
            # Step 1: Preprocess per chunk JSON
            print("Preprocessing chunk JSON...")
            pre_meta = self.preprocess_from_task_json(task_json)
            if pre_meta.get("skip"):
                return {"segments": [], "language": "unknown", "num_speakers": 0, "transcription_method": "diarized_segments_batched", "batch_size": batch_size}
            wav_path = pre_meta["out_wav_path"]
            base_offset_s = float(pre_meta.get("abs_start_ms", 0)) / 1000.0
            
            # Step 2: Transcribe full audio once
            transcription_results, detected_language = self.transcribe_full_audio(
                wav_path, language, translate, prompt, batch_size, base_offset_s=base_offset_s
            )

            # Step 3: Perform diarization with global offset
            diarization_segments, detected_num_speakers, speaker_embeddings = self.perform_diarization(
                wav_path, num_speakers, base_offset_s=base_offset_s
            )

            # Step 4: Merge diarization into transcription (assign speakers)
            transcription_results = self.assign_speakers_to_transcription(transcription_results, diarization_segments)
            
            # Step 5: Group segments if requested
            if group_segments:
                transcription_results = self.group_segments_by_speaker(transcription_results)
            
            # Step 6: Return results
            result = {
                "segments": transcription_results,
                "language": detected_language,
                "num_speakers": detected_num_speakers,
                "transcription_method": "diarized_segments_batched",
                "batch_size": batch_size,
                "speaker_embeddings": speaker_embeddings,
            }
            job_id = pre_meta["job_id"]
            task_id = pre_meta["chunk_idx"]
            filekey = f"ai-transcribe/split/{job_id}-{task_id}.json"
            ret = upload_data_to_r2(json.dumps(result), "intermediate", filekey)
            if ret:
                return {"filekey": filekey}
            else:
                return {"error": "Failed to upload to R2"}
                
        except Exception as e:
            import traceback
            traceback.print_exc()
            return {"error": f"Processing failed: {str(e)}"}
        finally:
            # Clean up preprocessed wav
            if pre_meta and pre_meta.get("out_wav_path") and os.path.exists(pre_meta["out_wav_path"]):
                try:
                    os.unlink(pre_meta["out_wav_path"])
                except Exception:
                    pass

# Initialize transcriber
transcriber = WhisperTranscriber()

def format_segments_for_display(result):
    """Format segments for display in Gradio"""
    if "error" in result:
        return f"❌ Error: {result['error']}"
        
    segments = result.get("segments", [])
    language = result.get("language", "unknown")
    num_speakers = result.get("num_speakers", 1)
    method = result.get("transcription_method", "unknown")
    batch_size = result.get("batch_size", "N/A")
    
    output = f"🎯 **Detection Results:**\n"
    output += f"- Language: {language}\n"
    output += f"- Speakers: {num_speakers}\n"
    output += f"- Segments: {len(segments)}\n"
    output += f"- Method: {method}\n"
    output += f"- Batch Size: {batch_size}\n\n"
    
    output += "📝 **Transcription:**\n\n"
    
    for i, segment in enumerate(segments, 1):
        start_time = str(datetime.timedelta(seconds=int(segment["start"])))
        end_time = str(datetime.timedelta(seconds=int(segment["end"])))
        speaker = segment.get("speaker", "SPEAKER_00")
        text = segment["text"]
        
        output += f"**{speaker}** ({start_time} → {end_time})\n"
        output += f"{text}\n\n"
        
    return output

@spaces.GPU
def process_audio_gradio(task_json, num_speakers, language, translate, prompt, group_segments, use_diarization, batch_size):
    """Gradio interface function"""
    if use_diarization:
        result = transcriber.process_audio(
            task_json=task_json,
            num_speakers=num_speakers if num_speakers > 0 else None,
            language=language if language != "auto" else None,
            translate=translate,
            prompt=prompt if prompt and prompt.strip() else None,
            group_segments=group_segments,
            batch_size=batch_size
        )
    else:
        result = transcriber.process_audio_full(
            task_json=task_json,
            language=language if language != "auto" else None,
            translate=translate,
            prompt=prompt if prompt and prompt.strip() else None,
            group_segments=group_segments,
            batch_size=batch_size
        )
    
    formatted_output = format_segments_for_display(result)
    return formatted_output, result

# Create Gradio interface
demo = gr.Blocks(
    title="🎙️ Whisper Transcription with Speaker Diarization",
    theme="default"
)

with demo:
    gr.Markdown("""
    # 🎙️ Advanced Audio Transcription & Speaker Diarization
    
    Upload an audio file to get accurate transcription with speaker identification, powered by:
    - **Faster-Whisper Large V3 Turbo** with batched inference for optimal performance
    - **Pyannote 3.1** for speaker diarization
    - **ZeroGPU** acceleration for optimal performance
    """)
    
    with gr.Row():
        with gr.Column():
            task_json_input = gr.Textbox(
                label="🧾 Paste Task JSON",
                placeholder="Paste the per-chunk task JSON here...",
                lines=16,
            )
            
            with gr.Accordion("⚙️ Advanced Settings", open=False):
                use_diarization = gr.Checkbox(
                    label="Enable Speaker Diarization",
                    value=True,
                    info="Uncheck for faster transcription without speaker identification"
                )
                
                batch_size = gr.Slider(
                    minimum=1,
                    maximum=128,
                    value=16,
                    step=1,
                    label="Batch Size",
                    info="Higher values = faster processing but more GPU memory usage. Recommended: 8-24"
                )
                
                num_speakers = gr.Slider(
                    minimum=0,
                    maximum=20,
                    value=0,
                    step=1,
                    label="Number of Speakers (0 = auto-detect)",
                    visible=True
                )
                
                language = gr.Dropdown(
                    choices=["auto", "en", "es", "fr", "de", "it", "pt", "ru", "ja", "ko", "zh"],
                    value="auto",
                    label="Language"
                )
                
                translate = gr.Checkbox(
                    label="Translate to English",
                    value=False
                )
                
                prompt = gr.Textbox(
                    label="Vocabulary Prompt (names, acronyms, etc.)",
                    placeholder="Enter names, technical terms, or context...",
                    lines=2
                )
                
                group_segments = gr.Checkbox(
                    label="Group segments by speaker/time",
                    value=True
                )
            
            process_btn = gr.Button("🚀 Transcribe Audio", variant="primary")
        
        with gr.Column():
            output_text = gr.Markdown(
                label="📝 Transcription Results",
                value="Paste task JSON and click 'Transcribe Audio' to get started!"
            )
            
            output_json = gr.JSON(
                label="🔧 Raw Output (JSON)",
                visible=False
            )
    
    # Update visibility of num_speakers based on diarization toggle
    use_diarization.change(
        fn=lambda x: gr.update(visible=x),
        inputs=[use_diarization],
        outputs=[num_speakers]
    )
    
    # Event handlers
    process_btn.click(
        fn=process_audio_gradio,
        inputs=[
            task_json_input,
            num_speakers,
            language,
            translate,
            prompt,
            group_segments,
            use_diarization,
            batch_size
        ],
        outputs=[output_text, output_json]
    )
    
    # Examples
    gr.Markdown("### 📋 Usage Tips:")
    gr.Markdown("""
    - Paste a single-chunk task JSON matching the preprocess schema
    - Batch Size: Higher values (16-24) = faster but uses more GPU memory
    - Speaker diarization: Enable for speaker identification (slower)
    - Languages: Supports 100+ languages with auto-detection
    - Vocabulary: Add names and technical terms in the prompt for better accuracy
    """)

if __name__ == "__main__":
    demo.launch(debug=True)