app.py

import spaces
import torch
import gradio as gr
import yt_dlp as youtube_dl
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import tempfile
import os
import time

# Environment and model configuration
hf_token = os.getenv('HF_TOKEN')
MODEL_NAME = "nyrahealth/CrisperWhisper"
BATCH_SIZE = 8
FILE_LIMIT_MB = 1000
YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files

# Device setup
device = 0 if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Timestamp adjustment function
def adjust_pauses_for_hf_pipeline_output(pipeline_output, split_threshold=0.12):
    """
    Adjust pause timings by distributing pauses up to the threshold evenly between adjacent words.
    """
    adjusted_chunks = pipeline_output["chunks"].copy()

    for i in range(len(adjusted_chunks) - 1):
        current_chunk = adjusted_chunks[i]
        next_chunk = adjusted_chunks[i + 1]

        current_start, current_end = current_chunk["timestamp"]
        next_start, next_end = next_chunk["timestamp"]
        pause_duration = next_start - current_end

        if pause_duration > 0:
            if pause_duration > split_threshold:
                distribute = split_threshold / 2
            else:
                distribute = pause_duration / 2

            # Adjust current chunk end time
            adjusted_chunks[i]["timestamp"] = (current_start, current_end + distribute)
            # Adjust next chunk start time
            adjusted_chunks[i + 1]["timestamp"] = (next_start - distribute, next_end)
    
    pipeline_output["chunks"] = adjusted_chunks
    return pipeline_output

# Initialize pipeline
pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    token=hf_token,
    torch_dtype=torch_dtype,
    chunk_length_s=30,
    device=device,
    return_timestamps='word',  # Enable word-level timestamps
)

# Transcribe function for microphone and file inputs
@spaces.GPU
def transcribe(inputs, task):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
    
    # Get full pipeline output
    raw_output = pipe(
        inputs, 
        batch_size=BATCH_SIZE, 
        generate_kwargs={"task": task}, 
        return_timestamps='word'
    )
    
    # Apply timestamp adjustment
    adjusted_output = adjust_pauses_for_hf_pipeline_output(raw_output)
    
    # Format output with timestamps
    formatted_text = ""
    for chunk in adjusted_output["chunks"]:
        start = chunk["timestamp"][0]
        text = chunk["text"]
        formatted_text += f"[{start:.2f}] {text}\n"
    
    return formatted_text

# YouTube HTML embed function
def _return_yt_html_embed(yt_url):
    video_id = yt_url.split("?v=")[-1]
    HTML_str = (
        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    return HTML_str

# YouTube audio download function
def download_yt_audio(yt_url, filename):
    info_loader = youtube_dl.YoutubeDL()
    
    try:
        info = info_loader.extract_info(yt_url, download=False)
    except youtube_dl.utils.DownloadError as err:
        raise gr.Error(str(err))
    
    file_length = info["duration_string"]
    file_h_m_s = file_length.split(":")
    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
    
    if len(file_h_m_s) == 1:
        file_h_m_s.insert(0, 0)
    if len(file_h_m_s) == 2:
        file_h_m_s.insert(0, 0)
    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
    
    if file_length_s > YT_LENGTH_LIMIT_S:
        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
    
    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
    
    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
        try:
            ydl.download([yt_url])
        except youtube_dl.utils.ExtractorError as err:
            raise gr.Error(str(err))

# Transcribe function for YouTube inputs
@spaces.GPU
def yt_transcribe(yt_url, task, max_filesize=75.0):
    html_embed_str = _return_yt_html_embed(yt_url)

    with tempfile.TemporaryDirectory() as tmpdirname:
        filepath = os.path.join(tmpdirname, "video.mp4")
        download_yt_audio(yt_url, filepath)
        with open(filepath, "rb") as f:
            inputs = f.read()

    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}

    # Get full pipeline output
    raw_output = pipe(
        inputs,
        batch_size=BATCH_SIZE,
        generate_kwargs={"task": task},
        return_timestamps='word'
    )
    
    # Apply timestamp adjustment
    adjusted_output = adjust_pauses_for_hf_pipeline_output(raw_output)
    
    # Format output with timestamps
    formatted_text = ""
    for chunk in adjusted_output["chunks"]:
        start = chunk["timestamp"][0]
        text = chunk["text"]
        formatted_text += f"[{start:.2f}] {text}\n"
    
    return html_embed_str, formatted_text

# Gradio interface setup
demo = gr.Blocks()

mf_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="microphone", type="filepath"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs="text",
    title="CrisperWhisper: Transcribe Audio as it is",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
        f" checkpoint [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
        " of arbitrary length."
    ),
    allow_flagging="never",
)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources="upload", type="filepath", label="Audio file"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
    ],
    outputs="text",
    title="CrisperWhisper: Transcribe Audio as it is",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
        f" checkpoint [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
        " of arbitrary length."
    ),
    allow_flagging="never",
)

yt_transcribe = gr.Interface(
    fn=yt_transcribe,
    inputs=[
        gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
    ],
    outputs=["html", "text"],
    title="CrisperWhisper: Transcribe Audio as it is",
    description=(
        "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint"
        f" [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) and 🤗 Transformers to transcribe video files of"
        " arbitrary length."
    ),
    allow_flagging="never",
)

# Combine interfaces into a tabbed layout
with demo:
    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])

# Launch the app
demo.queue().launch()