Spaces:
Running
on
A10G
Running
on
A10G
File size: 4,063 Bytes
7bcf8d7 0a6cd62 20d488d 7bcf8d7 50a74e8 7bcf8d7 5442f52 90945f2 5442f52 4090e0d 7bcf8d7 d3faf34 d4e3aaf d3faf34 d4e3aaf bcdaadd 9fea840 d4e3aaf d3faf34 9f061d9 90945f2 7bcf8d7 1b7f5da 7bcf8d7 90945f2 6f53272 7bcf8d7 07fc2ac 7bcf8d7 a45002a 90945f2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
import librosa
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
import numpy as np
from pathlib import Path
from huggingface_hub import hf_hub_download
from torchaudio.models.decoder import ctc_decoder
ASR_SAMPLING_RATE = 16_000
ASR_LANGUAGES = {}
with open(f"data/asr/all_langs.tsv") as f:
for line in f:
iso, name = line.split(" ", 1)
ASR_LANGUAGES[iso.strip()] = name.strip()
MODEL_ID = "facebook/mms-1b-all"
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# lm_decoding_config = {}
# lm_decoding_configfile = hf_hub_download(
# repo_id="facebook/mms-cclms",
# filename="decoding_config.json",
# subfolder="mms-1b-all",
# )
# with open(lm_decoding_configfile) as f:
# lm_decoding_config = json.loads(f.read())
# # allow language model decoding for "eng"
# decoding_config = lm_decoding_config["eng"]
# lm_file = hf_hub_download(
# repo_id="facebook/mms-cclms",
# filename=decoding_config["lmfile"].rsplit("/", 1)[1],
# subfolder=decoding_config["lmfile"].rsplit("/", 1)[0],
# )
# token_file = hf_hub_download(
# repo_id="facebook/mms-cclms",
# filename=decoding_config["tokensfile"].rsplit("/", 1)[1],
# subfolder=decoding_config["tokensfile"].rsplit("/", 1)[0],
# )
# lexicon_file = None
# if decoding_config["lexiconfile"] is not None:
# lexicon_file = hf_hub_download(
# repo_id="facebook/mms-cclms",
# filename=decoding_config["lexiconfile"].rsplit("/", 1)[1],
# subfolder=decoding_config["lexiconfile"].rsplit("/", 1)[0],
# )
# beam_search_decoder = ctc_decoder(
# lexicon=lexicon_file,
# tokens=token_file,
# lm=lm_file,
# nbest=1,
# beam_size=500,
# beam_size_token=50,
# lm_weight=float(decoding_config["lmweight"]),
# word_score=float(decoding_config["wordscore"]),
# sil_score=float(decoding_config["silweight"]),
# blank_token="<s>",
# )
def transcribe(audio_data=None, lang="eng (English)"):
if not audio_data:
return "<<ERROR: Empty Audio Input>>"
if isinstance(audio_data, tuple):
# microphone
sr, audio_samples = audio_data
audio_samples = (audio_samples / 32768.0).astype(np.float32)
if sr != ASR_SAMPLING_RATE:
audio_samples = librosa.resample(
audio_samples, orig_sr=sr, target_sr=ASR_SAMPLING_RATE
)
else:
# file upload
if not isinstance(audio_data, str):
return "<<ERROR: Invalid Audio Input Instance: {}>>".format(type(audio_data))
audio_samples = librosa.load(audio_data, sr=ASR_SAMPLING_RATE, mono=True)[0]
lang_code = lang.split()[0]
processor.tokenizer.set_target_lang(lang_code)
model.load_adapter(lang_code)
inputs = processor(
audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt"
)
# set device
if torch.cuda.is_available():
device = torch.device("cuda")
elif (
hasattr(torch.backends, "mps")
and torch.backends.mps.is_available()
and torch.backends.mps.is_built()
):
device = torch.device("mps")
else:
device = torch.device("cpu")
model.to(device)
inputs = inputs.to(device)
with torch.no_grad():
outputs = model(**inputs).logits
if lang_code != "eng" or True:
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
else:
assert False
# beam_search_result = beam_search_decoder(outputs.to("cpu"))
# transcription = " ".join(beam_search_result[0][0].words).strip()
return transcription
ASR_EXAMPLES = [
["upload/english.mp3", "eng (English)"],
# ["upload/tamil.mp3", "tam (Tamil)"],
# ["upload/burmese.mp3", "mya (Burmese)"],
]
ASR_NOTE = """
The above demo doesn't use beam-search decoding using a language model.
Checkout the instructions [here](https://huggingface.co/facebook/mms-1b-all) on how to run LM decoding for better accuracy.
""" |