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import os
import tempfile
import time
import gradio as gr
import numpy as np
import torch
import yt_dlp as youtube_dl
from gradio_client import Client
from pyannote.audio import Pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
YT_LENGTH_LIMIT_S = 36000 # limit to 1 hour YouTube files
SAMPLING_RATE = 16000
API_URL = "https://sanchit-gandhi-whisper-jax.hf.space/"
# set up the Gradio client
client = Client(API_URL)
# set up the diarization pipeline
diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=True)
def format_string(timestamp):
"""
Reformat a timestamp string from (HH:)MM:SS to float seconds. Note that the hour column
is optional, and is appended within the function if not input.
Args:
timestamp (str):
Timestamp in string format, either MM:SS or HH:MM:SS.
Returns:
seconds (float):
Total seconds corresponding to the input timestamp.
"""
split_time = timestamp.split(":")
split_time = [float(sub_time) for sub_time in split_time]
if len(split_time) == 2:
split_time.insert(0, 0)
seconds = split_time[0] * 3600 + split_time[1] * 60 + split_time[2]
return seconds
# Adapted from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
"""
Reformat a timestamp from a float of seconds to a string in format (HH:)MM:SS. Note that the hour
column is optional, and is appended in the function if the number of hours > 0.
Args:
seconds (float):
Total seconds corresponding to the input timestamp.
Returns:
timestamp (str):
Timestamp in string format, either MM:SS or HH:MM:SS.
"""
if seconds is not None:
milliseconds = round(seconds * 1000.0)
hours = milliseconds // 3_600_000
milliseconds -= hours * 3_600_000
minutes = milliseconds // 60_000
milliseconds -= minutes * 60_000
seconds = milliseconds // 1_000
milliseconds -= seconds * 1_000
hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
else:
# we have a malformed timestamp so just return it as is
return seconds
def format_as_transcription(raw_segments):
return "\n".join(
[
f"{chunk['speaker']} [{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}"
for chunk in raw_segments
]
)
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
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_length_s = format_string(file_length)
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))
def align(transcription, segments, group_by_speaker=True):
transcription_split = transcription.split("\n")
# re-format transcription from string to List[Dict]
transcript = []
for chunk in transcription_split:
start_end, transcription = chunk[1:].split("] ")
start, end = start_end.split("->")
transcript.append({"timestamp": (format_string(start), format_string(end)), "text": transcription})
# diarizer output may contain consecutive segments from the same speaker (e.g. {(0 -> 1, speaker_1), (1 -> 1.5, speaker_1), ...})
# we combine these segments to give overall timestamps for each speaker's turn (e.g. {(0 -> 1.5, speaker_1), ...})
new_segments = []
prev_segment = cur_segment = segments[0]
for i in range(1, len(segments)):
cur_segment = segments[i]
# check if we have changed speaker ("label")
if cur_segment["label"] != prev_segment["label"] and i < len(segments):
# add the start/end times for the super-segment to the new list
new_segments.append(
{
"segment": {"start": prev_segment["segment"]["start"], "end": cur_segment["segment"]["start"]},
"speaker": prev_segment["label"],
}
)
prev_segment = segments[i]
# add the last segment(s) if there was no speaker change
new_segments.append(
{
"segment": {"start": prev_segment["segment"]["start"], "end": cur_segment["segment"]["end"]},
"speaker": prev_segment["label"],
}
)
# get the end timestamps for each chunk from the ASR output
end_timestamps = np.array([chunk["timestamp"][-1] for chunk in transcript])
segmented_preds = []
# align the diarizer timestamps and the ASR timestamps
for segment in new_segments:
# get the diarizer end timestamp
end_time = segment["segment"]["end"]
# find the ASR end timestamp that is closest to the diarizer's end timestamp and cut the transcript to here
upto_idx = np.argmin(np.abs(end_timestamps - end_time))
if group_by_speaker:
segmented_preds.append(
{
"speaker": segment["speaker"],
"text": "".join([chunk["text"] for chunk in transcript[: upto_idx + 1]]),
"timestamp": (transcript[0]["timestamp"][0], transcript[upto_idx]["timestamp"][1]),
}
)
else:
for i in range(upto_idx + 1):
segmented_preds.append({"speaker": segment["speaker"], **transcript[i]})
# crop the transcripts and timestamp lists according to the latest timestamp (for faster argmin)
transcript = transcript[upto_idx + 1 :]
end_timestamps = end_timestamps[upto_idx + 1 :]
# final post-processing
transcription = format_as_transcription(segmented_preds)
return transcription
def transcribe(audio_path, group_by_speaker=True):
# run Whisper JAX asynchronously using Gradio client (endpoint)
job = client.submit(
audio_path,
"transcribe",
True,
api_name="/predict_1",
)
# run diarization while we wait for Whisper JAX
diarization = diarization_pipeline(audio_path)
segments = diarization.for_json()["content"]
# only fetch the transcription result after performing diarization
transcription, _ = job.result()
# align the ASR transcriptions and diarization timestamps
transcription = align(transcription, segments, group_by_speaker=group_by_speaker)
return transcription
def transcribe_yt(yt_url, group_by_speaker=True):
# run Whisper JAX asynchronously using Gradio client (endpoint)
job = client.submit(
yt_url,
"transcribe",
True,
api_name="/predict_2",
)
_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, SAMPLING_RATE)
inputs = torch.from_numpy(inputs).float()
inputs = inputs.unsqueeze(0)
diarization = diarization_pipeline(
{"waveform": inputs, "sample_rate": SAMPLING_RATE},
)
segments = diarization.for_json()["content"]
# only fetch the transcription result after performing diarization
transcription, _ = job.result()
# align the ASR transcriptions and diarization timestamps
transcription = align(transcription, segments, group_by_speaker=group_by_speaker)
return transcription
title = "Whisper JAX + Speaker Diarization ⚡️"
description = """Combine the speed of Whisper JAX with pyannote speaker diarization to transcribe meetings in super fast time.
"""
article = "Whisper large-v2 model by OpenAI. Speaker diarization model by pyannote. Whisper JAX backend running JAX on a TPU v4-8 through the generous support of the [TRC](https://sites.research.google/trc/about/) programme. Whisper JAX [code](https://github.com/sanchit-gandhi/whisper-jax) and Gradio demo by 🤗 Hugging Face."
microphone = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", optional=True, type="filepath"),
gr.inputs.Checkbox(default=True, label="Group by speaker"),
],
outputs=[
gr.outputs.Textbox(label="Transcription").style(show_copy_button=True),
],
allow_flagging="never",
title=title,
description=description,
article=article,
)
audio_file = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"),
gr.inputs.Checkbox(default=True, label="Group by speaker"),
],
outputs=[
gr.outputs.Textbox(label="Transcription").style(show_copy_button=True),
],
allow_flagging="never",
title=title,
description=description,
article=article,
)
youtube = gr.Interface(
fn=transcribe_yt,
inputs=[
gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
gr.inputs.Checkbox(default=True, label="Group by speaker"),
],
outputs=[
gr.outputs.HTML(label="Video"),
gr.outputs.Textbox(label="Transcription").style(show_copy_button=True),
],
allow_flagging="never",
title=title,
examples=[["https://www.youtube.com/watch?v=m8u-18Q0s7I", True]],
cache_examples=False,
description=description,
article=article,
)
demo = gr.Blocks()
with demo:
gr.TabbedInterface([microphone, audio_file, youtube], ["Microphone", "Audio File", "YouTube"])
demo.queue(concurrency_count=1, max_size=5)
demo.launch()
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