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from collections import deque
import streamlit as st
import torch
from streamlit_player import st_player
from transformers import AutoModelForCTC, Wav2Vec2Processor
from streaming import ffmpeg_stream
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
player_options = {
"events": ["onProgress"],
"progress_interval": 200,
"volume": 1.0,
"playing": True,
"loop": False,
"controls": False,
"muted": False,
"config": {"youtube": {"playerVars": {"start": 1}}},
}
st.title("YouTube Video Spanish ASR")
st.write("Acknowledgement: This demo is based on Anton Lozhkov's cool Space : https://huggingface.co/spaces/anton-l/youtube-subs-wav2vec")
# disable rapid fading in and out on `st.code` updates
st.markdown("<style>.element-container{opacity:1 !important}</style>", unsafe_allow_html=True)
@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
def load_model(model_path="facebook/wav2vec2-large-robust-ft-swbd-300h"):
processor = Wav2Vec2Processor.from_pretrained(model_path)
model = AutoModelForCTC.from_pretrained(model_path).to(device)
return processor, model
model_path = st.radio(
"Select a model", (
"jonatasgrosman/wav2vec2-xls-r-1b-spanish",
"jonatasgrosman/wav2vec2-large-xlsr-53-spanish",
"patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm",
"facebook/wav2vec2-large-xlsr-53-spanish",
"glob-asr/xls-r-es-test-lm"
)
)
processor, model = load_model(model_path)
def stream_text(url, chunk_duration_ms, pad_duration_ms):
sampling_rate = processor.feature_extractor.sampling_rate
# calculate the length of logits to cut from the sides of the output to account for input padding
output_pad_len = model._get_feat_extract_output_lengths(int(sampling_rate * pad_duration_ms / 1000))
# define the audio chunk generator
stream = ffmpeg_stream(url, sampling_rate, chunk_duration_ms=chunk_duration_ms, pad_duration_ms=pad_duration_ms)
leftover_text = ""
for i, chunk in enumerate(stream):
input_values = processor(chunk, sampling_rate=sampling_rate, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values.to(device)).logits[0]
if i > 0:
logits = logits[output_pad_len : len(logits) - output_pad_len]
else: # don't count padding at the start of the clip
logits = logits[: len(logits) - output_pad_len]
predicted_ids = torch.argmax(logits, dim=-1).cpu().tolist()
if processor.decode(predicted_ids).strip():
leftover_ids = processor.tokenizer.encode(leftover_text)
# concat the last word (or its part) from the last frame with the current text
text = processor.decode(leftover_ids + predicted_ids)
# don't return the last word in case it's just partially recognized
if " " in text:
text, leftover_text = text.rsplit(" ", 1)
else:
leftover_text = text
text = ""
if text:
yield text
else:
yield leftover_text
leftover_text = ""
yield leftover_text
def main():
state = st.session_state
st.header("Video ASR Streamlit from Youtube Link")
with st.form(key="inputs_form"):
initial_url = "https://youtu.be/ghOqTkGzX7I?t=60"
state.youtube_url = st.text_input("YouTube URL", initial_url)
state.chunk_duration_ms = st.slider("Audio chunk duration (ms)", 2000, 10000, 3000, 100)
state.pad_duration_ms = st.slider("Padding duration (ms)", 100, 5000, 1000, 100)
submit_button = st.form_submit_button(label="Submit")
if submit_button or "asr_stream" not in state:
# a hack to update the video player on value changes
state.youtube_url = (
state.youtube_url.split("&hash=")[0]
+ f"&hash={state.chunk_duration_ms}-{state.pad_duration_ms}"
)
state.asr_stream = stream_text(
state.youtube_url, state.chunk_duration_ms, state.pad_duration_ms
)
state.chunks_taken = 0
state.lines = deque([], maxlen=5) # limit to the last n lines of subs
player = st_player(state.youtube_url, **player_options, key="youtube_player")
if "asr_stream" in state and player.data and player.data["played"] < 1.0:
# check how many seconds were played, and if more than processed - write the next text chunk
processed_seconds = state.chunks_taken * (state.chunk_duration_ms / 1000)
if processed_seconds < player.data["playedSeconds"]:
text = next(state.asr_stream)
state.lines.append(text)
state.chunks_taken += 1
if "lines" in state:
# print the lines of subs
st.code("\n".join(state.lines))
if __name__ == "__main__":
main() |