Hugging Face's logo

Spaces:
Afrinetwork
/
sts
Sleeping

App Files Community
sts
File size: 3,924 Bytes 
7bcf8d7
 
 
0a6cd62
7bcf8d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5442f52
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
90945f2
5442f52
 
 
 
 
 
 
 
 
 
 
 
4090e0d
7bcf8d7
d3faf34
d4e3aaf
d3faf34
 
 
d4e3aaf
 
 
bcdaadd
9fea840
 
 
 
d4e3aaf
 
d3faf34
d4e3aaf
90945f2
7bcf8d7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1b7f5da
7bcf8d7
 
 
90945f2
6f53272
 
7bcf8d7
 
 
 
 
cd75560
 
 
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
 
import librosa
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
import numpy as np

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] = name

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
        
        isinstance(audio_data, str)
        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 = [
    ["assets/english.mp3", "eng (English)"],
    # ["assets/tamil.mp3", "tam (Tamil)"],
    # ["assets/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.
"""