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David Pomerenke commited on
Commit
1ab3999
·
1 Parent(s): c4c59ec

Basic FLEURS transcription setup

Browse files

Not yet integrated into benchmarking

Files changed (5) hide show
  1. .gitignore +0 -1
  2. evals.py +75 -23
  3. pyproject.toml +4 -0
  4. results.json +38 -19
  5. uv.lock +416 -1
.gitignore CHANGED
@@ -1,6 +1,5 @@
1
  floresp-*
2
  glottolog-*
3
- *.m4a
4
  LanguageCodes.tab
5
  ScriptCodes.csv
6
  .cache
 
1
  floresp-*
2
  glottolog-*
 
3
  LanguageCodes.tab
4
  ScriptCodes.csv
5
  .cache
evals.py CHANGED
@@ -1,4 +1,5 @@
1
  import asyncio
 
2
  import json
3
  import os
4
  import random
@@ -9,9 +10,12 @@ from os import getenv
9
  import evaluate
10
  import pandas as pd
11
  import requests
 
12
  from aiolimiter import AsyncLimiter
 
13
  from dotenv import load_dotenv
14
  from elevenlabs import ElevenLabs
 
15
  from joblib.memory import Memory
16
  from langcodes import Language, standardize_tag
17
  from language_data.population_data import LANGUAGE_SPEAKING_POPULATION
@@ -21,7 +25,6 @@ from requests import get
21
  from rich import print
22
  from tqdm.asyncio import tqdm_asyncio
23
  from transformers import NllbTokenizer
24
- from huggingface_hub import InferenceClient
25
 
26
  # config
27
  models = [
@@ -35,6 +38,11 @@ models = [
35
  "microsoft/phi-4", # 0.07$/M tokens
36
  ]
37
  fast_model = "meta-llama/llama-3.3-70b-instruct"
 
 
 
 
 
38
  n_sentences = 30
39
 
40
  # setup
@@ -46,32 +54,11 @@ client = AsyncOpenAI(
46
  cache = Memory(location=".cache", verbose=0).cache
47
  bleu = evaluate.load("bleu")
48
  chrf = evaluate.load("chrf")
 
49
  tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
50
  rate_limit = AsyncLimiter(max_rate=20, time_period=1)
51
 
52
 
53
- @cache
54
- def transcribe(filename, model="elevenlabs/scribe_v1"):
55
- provider, modelname = model.split("/")
56
- with open(filename, "rb") as f:
57
- audio = f.read()
58
- match provider:
59
- case "elevenlabs":
60
- client = ElevenLabs(api_key=getenv("ELEVENLABS_API_KEY"))
61
- response = client.speech_to_text.convert(model_id=modelname, file=audio)
62
- return response.text
63
- case "openai":
64
- client = InferenceClient(api_key=getenv("HUGGINGFACE_ACCESS_TOKEN"))
65
- output = client.automatic_speech_recognition(model=model, audio=audio)
66
- return output.text
67
- case _:
68
- raise ValueError(f"Model {model} not supported")
69
-
70
-
71
- print(transcribe("data/test.m4a", "openai/whisper-large-v3-turbo"))
72
- exit()
73
-
74
-
75
  # load general language data
76
  languages = {
77
  lang: pop
@@ -142,6 +129,61 @@ benchmark_languages = (
142
  .reset_index()
143
  )
144
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
145
 
146
  # load CommonVoice stats
147
  @cache # cache for 1 day
@@ -169,6 +211,9 @@ commonvoice_stats = (
169
  languages = pd.merge(
170
  languages, benchmark_languages, on="bcp_47", how="left"
171
  ) # "left" because keep it simple for now
 
 
 
172
  languages = pd.merge(
173
  languages, commonvoice_stats, on="bcp_47", how="left"
174
  ) # "left" because keep it simple for now
@@ -355,6 +400,12 @@ async def mlm_and_evaluate(model, language_bcp_47, nr):
355
  }
356
 
357
 
 
 
 
 
 
 
358
  def mean(lst):
359
  return sum(lst) / len(lst) if lst else 0
360
 
@@ -453,6 +504,7 @@ async def main():
453
  "language_family": language_family(
454
  language.flores_path.split("_")[0]
455
  ),
 
456
  }
457
  )
458
  with open("results.json", "w") as f:
 
1
  import asyncio
2
+ import io
3
  import json
4
  import os
5
  import random
 
10
  import evaluate
11
  import pandas as pd
12
  import requests
13
+ import soundfile as sf
14
  from aiolimiter import AsyncLimiter
15
+ from datasets import load_dataset
16
  from dotenv import load_dotenv
17
  from elevenlabs import ElevenLabs
18
+ from huggingface_hub import InferenceClient
19
  from joblib.memory import Memory
20
  from langcodes import Language, standardize_tag
21
  from language_data.population_data import LANGUAGE_SPEAKING_POPULATION
 
25
  from rich import print
26
  from tqdm.asyncio import tqdm_asyncio
27
  from transformers import NllbTokenizer
 
28
 
29
  # config
30
  models = [
 
38
  "microsoft/phi-4", # 0.07$/M tokens
39
  ]
40
  fast_model = "meta-llama/llama-3.3-70b-instruct"
41
+ transcription_models = [
42
+ "elevenlabs/scribe_v1",
43
+ "openai/whisper-large-v3-turbo",
44
+ "openai/whisper-tiny",
45
+ ]
46
  n_sentences = 30
47
 
48
  # setup
 
54
  cache = Memory(location=".cache", verbose=0).cache
55
  bleu = evaluate.load("bleu")
56
  chrf = evaluate.load("chrf")
57
+ wer = evaluate.load("wer")
58
  tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
59
  rate_limit = AsyncLimiter(max_rate=20, time_period=1)
60
 
61
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
62
  # load general language data
63
  languages = {
64
  lang: pop
 
129
  .reset_index()
130
  )
131
 
132
+ fleurs_tags = "af_za,am_et,ar_eg,as_in,ast_es,az_az,be_by,bg_bg,bn_in,bs_ba,ca_es,ceb_ph,ckb_iq,cmn_hans_cn,cs_cz,cy_gb,da_dk,de_de,el_gr,en_us,es_419,et_ee,fa_ir,ff_sn,fi_fi,fil_ph,fr_fr,ga_ie,gl_es,gu_in,ha_ng,he_il,hi_in,hr_hr,hu_hu,hy_am,id_id,ig_ng,is_is,it_it,ja_jp,jv_id,ka_ge,kam_ke,kea_cv,kk_kz,km_kh,kn_in,ko_kr,ky_kg,lb_lu,lg_ug,ln_cd,lo_la,lt_lt,luo_ke,lv_lv,mi_nz,mk_mk,ml_in,mn_mn,mr_in,ms_my,mt_mt,my_mm,nb_no,ne_np,nl_nl,nso_za,ny_mw,oc_fr,om_et,or_in,pa_in,pl_pl,ps_af,pt_br,ro_ro,ru_ru,sd_in,sk_sk,sl_si,sn_zw,so_so,sr_rs,sv_se,sw_ke,ta_in,te_in,tg_tj,th_th,tr_tr,uk_ua,umb_ao,ur_pk,uz_uz,vi_vn,wo_sn,xh_za,yo_ng,yue_hant_hk,zu_za".split(
133
+ ","
134
+ )
135
+ fleurs = pd.DataFrame(fleurs_tags, columns=["fleurs_tag"])
136
+ fleurs["bcp_47"] = fleurs["fleurs_tag"].apply(
137
+ lambda x: standardize_tag(x.rsplit("_")[0], macro=True)
138
+ )
139
+
140
+
141
+ @cache
142
+ def transcribe(file, model="elevenlabs/scribe_v1"):
143
+ provider, modelname = model.split("/")
144
+ match provider:
145
+ case "elevenlabs":
146
+ client = ElevenLabs(api_key=getenv("ELEVENLABS_API_KEY"))
147
+ response = client.speech_to_text.convert(model_id=modelname, file=file)
148
+ return response.text
149
+ case "openai":
150
+ client = InferenceClient(api_key=getenv("HUGGINGFACE_ACCESS_TOKEN"))
151
+ output = client.automatic_speech_recognition(model=model, audio=file)
152
+ return output.text
153
+ case _:
154
+ raise ValueError(f"Model {model} not supported")
155
+
156
+
157
+ def audio_array_to_bytes(array, sample_rate=16000):
158
+ # convert audio array to WAV bytes
159
+ audio_bytes = io.BytesIO()
160
+ sf.write(audio_bytes, array, sample_rate, format="wav")
161
+ audio_bytes.seek(0)
162
+ return audio_bytes.getvalue()
163
+
164
+
165
+ for tag in ["de_de"]:
166
+ print(tag)
167
+ fleurs = load_dataset(
168
+ "google/fleurs",
169
+ tag,
170
+ split="validation",
171
+ trust_remote_code=True,
172
+ streaming=True,
173
+ )
174
+ item = next(iter(fleurs))
175
+ file = audio_array_to_bytes(item["audio"]["array"])
176
+ true = item["raw_transcription"]
177
+ print(true)
178
+ for model in transcription_models:
179
+ print(model)
180
+ pred = transcribe(file, model=model)
181
+ print(pred)
182
+ score = wer.compute(predictions=[pred], references=[true])
183
+ print(f"WER: {score}")
184
+ print("-" * 100)
185
+ exit()
186
+
187
 
188
  # load CommonVoice stats
189
  @cache # cache for 1 day
 
211
  languages = pd.merge(
212
  languages, benchmark_languages, on="bcp_47", how="left"
213
  ) # "left" because keep it simple for now
214
+ languages = pd.merge(
215
+ languages, fleurs, on="bcp_47", how="left"
216
+ ) # "left" because keep it simple for now
217
  languages = pd.merge(
218
  languages, commonvoice_stats, on="bcp_47", how="left"
219
  ) # "left" because keep it simple for now
 
400
  }
401
 
402
 
403
+ @cache
404
+ async def transcribe_and_evaluate(model, language_bcp_47, nr):
405
+ language = languages[languages["bcp_47"] == language_bcp_47].iloc[0]
406
+ sentences = pd.DataFrame(load_sentences(language), columns=["text"])
407
+
408
+
409
  def mean(lst):
410
  return sum(lst) / len(lst) if lst else 0
411
 
 
504
  "language_family": language_family(
505
  language.flores_path.split("_")[0]
506
  ),
507
+ "fleurs_tag": language.fleurs_tag,
508
  }
509
  )
510
  with open("results.json", "w") as f:
pyproject.toml CHANGED
@@ -15,17 +15,21 @@ dependencies = [
15
  dev-dependencies = [
16
  "aiolimiter>=1.1.0",
17
  "bert-score>=0.3.13",
 
18
  "elevenlabs>=1.53.0",
19
  "evaluate==0.4.0",
20
  "huggingface-hub>=0.29.1",
 
21
  "joblib>=1.4.2",
22
  "langcodes>=3.5.0",
 
23
  "openai>=1.52.2",
24
  "protobuf>=5.28.3",
25
  "pyglottolog>=3.14.0",
26
  "python-dotenv>=1.0.1",
27
  "sacrebleu>=2.4.3",
28
  "sentencepiece>=0.2.0",
 
29
  "tiktoken>=0.8.0",
30
  "tqdm>=4.66.6",
31
  "transformers>=4.46.1",
 
15
  dev-dependencies = [
16
  "aiolimiter>=1.1.0",
17
  "bert-score>=0.3.13",
18
+ "datasets>=3.0.2",
19
  "elevenlabs>=1.53.0",
20
  "evaluate==0.4.0",
21
  "huggingface-hub>=0.29.1",
22
+ "jiwer>=3.1.0",
23
  "joblib>=1.4.2",
24
  "langcodes>=3.5.0",
25
+ "librosa>=0.10.2.post1",
26
  "openai>=1.52.2",
27
  "protobuf>=5.28.3",
28
  "pyglottolog>=3.14.0",
29
  "python-dotenv>=1.0.1",
30
  "sacrebleu>=2.4.3",
31
  "sentencepiece>=0.2.0",
32
+ "soundfile>=0.13.1",
33
  "tiktoken>=0.8.0",
34
  "tqdm>=4.66.6",
35
  "transformers>=4.46.1",
results.json CHANGED
@@ -209,7 +209,8 @@
209
  "ZM": 2788256,
210
  "ZW": 6109446
211
  },
212
- "language_family": "Indo-European"
 
213
  },
214
  {
215
  "language_name": "Chinese",
@@ -254,7 +255,8 @@
254
  "US": 2295209,
255
  "VN": 1085934
256
  },
257
- "language_family": "Sino-Tibetan"
 
258
  },
259
  {
260
  "language_name": "Hindi",
@@ -285,7 +287,8 @@
285
  "UG": 2206,
286
  "ZA": 1129272
287
  },
288
- "language_family": "Indo-European"
 
289
  },
290
  {
291
  "language_name": "Spanish",
@@ -349,7 +352,8 @@
349
  "UY": 2981097,
350
  "VE": 23488572
351
  },
352
- "language_family": "Indo-European"
 
353
  },
354
  {
355
  "language_name": "Arabic",
@@ -412,7 +416,8 @@
412
  "TR": 459298,
413
  "YE": 22114456
414
  },
415
- "language_family": "Afro-Asiatic"
 
416
  },
417
  {
418
  "language_name": "Urdu",
@@ -442,7 +447,8 @@
442
  "MU": 71727,
443
  "PK": 221825950
444
  },
445
- "language_family": "Indo-European"
 
446
  },
447
  {
448
  "language_name": "French",
@@ -529,7 +535,8 @@
529
  "WF": 7610,
530
  "YT": 110580
531
  },
532
- "language_family": "Indo-European"
 
533
  },
534
  {
535
  "language_name": "Bangla",
@@ -559,7 +566,8 @@
559
  "IN": 107413290,
560
  "NP": 28508
561
  },
562
- "language_family": "Indo-European"
 
563
  },
564
  {
565
  "language_name": "Portuguese",
@@ -600,7 +608,8 @@
600
  "ST": 179454,
601
  "TL": 816395
602
  },
603
- "language_family": "Indo-European"
 
604
  },
605
  {
606
  "language_name": "Punjabi",
@@ -631,7 +640,8 @@
631
  "PK": 163450700,
632
  "SG": 9314
633
  },
634
- "language_family": "Indo-European"
 
635
  },
636
  {
637
  "language_name": "Russian",
@@ -679,7 +689,8 @@
679
  "US": 798334,
680
  "UZ": 4279156
681
  },
682
- "language_family": "Indo-European"
 
683
  },
684
  {
685
  "language_name": "Swahili",
@@ -713,7 +724,8 @@
713
  "YT": 2716,
714
  "ZA": 1016
715
  },
716
- "language_family": "Atlantic-Congo"
 
717
  },
718
  {
719
  "language_name": "Indonesian",
@@ -740,7 +752,8 @@
740
  "ID": 170896640,
741
  "NL": 311047
742
  },
743
- "language_family": "Austronesian"
 
744
  },
745
  {
746
  "language_name": "German",
@@ -792,7 +805,8 @@
792
  "SK": 1196932,
793
  "US": 1563403
794
  },
795
- "language_family": "Indo-European"
 
796
  },
797
  {
798
  "language_name": "Japanese",
@@ -820,7 +834,8 @@
820
  "CA": 52772,
821
  "JP": 119231650
822
  },
823
- "language_family": "Japonic"
 
824
  },
825
  {
826
  "language_name": "Telugu",
@@ -846,7 +861,8 @@
846
  "population": {
847
  "IN": 95478480
848
  },
849
- "language_family": "Dravidian"
 
850
  },
851
  {
852
  "language_name": "Marathi",
@@ -872,7 +888,8 @@
872
  "population": {
873
  "IN": 92826300
874
  },
875
- "language_family": "Indo-European"
 
876
  },
877
  {
878
  "language_name": "Javanese",
@@ -899,7 +916,8 @@
899
  "ID": 90788840,
900
  "MY": 391825
901
  },
902
- "language_family": "Austronesian"
 
903
  },
904
  {
905
  "language_name": "Vietnamese",
@@ -928,6 +946,7 @@
928
  "US": 1130973,
929
  "VN": 84900318
930
  },
931
- "language_family": "Austroasiatic"
 
932
  }
933
  ]
 
209
  "ZM": 2788256,
210
  "ZW": 6109446
211
  },
212
+ "language_family": "Indo-European",
213
+ "fleurs_tag": "en_us"
214
  },
215
  {
216
  "language_name": "Chinese",
 
255
  "US": 2295209,
256
  "VN": 1085934
257
  },
258
+ "language_family": "Sino-Tibetan",
259
+ "fleurs_tag": "cmn_hans_cn"
260
  },
261
  {
262
  "language_name": "Hindi",
 
287
  "UG": 2206,
288
  "ZA": 1129272
289
  },
290
+ "language_family": "Indo-European",
291
+ "fleurs_tag": "hi_in"
292
  },
293
  {
294
  "language_name": "Spanish",
 
352
  "UY": 2981097,
353
  "VE": 23488572
354
  },
355
+ "language_family": "Indo-European",
356
+ "fleurs_tag": "es_419"
357
  },
358
  {
359
  "language_name": "Arabic",
 
416
  "TR": 459298,
417
  "YE": 22114456
418
  },
419
+ "language_family": "Afro-Asiatic",
420
+ "fleurs_tag": "ar_eg"
421
  },
422
  {
423
  "language_name": "Urdu",
 
447
  "MU": 71727,
448
  "PK": 221825950
449
  },
450
+ "language_family": "Indo-European",
451
+ "fleurs_tag": "ur_pk"
452
  },
453
  {
454
  "language_name": "French",
 
535
  "WF": 7610,
536
  "YT": 110580
537
  },
538
+ "language_family": "Indo-European",
539
+ "fleurs_tag": "fr_fr"
540
  },
541
  {
542
  "language_name": "Bangla",
 
566
  "IN": 107413290,
567
  "NP": 28508
568
  },
569
+ "language_family": "Indo-European",
570
+ "fleurs_tag": "bn_in"
571
  },
572
  {
573
  "language_name": "Portuguese",
 
608
  "ST": 179454,
609
  "TL": 816395
610
  },
611
+ "language_family": "Indo-European",
612
+ "fleurs_tag": "pt_br"
613
  },
614
  {
615
  "language_name": "Punjabi",
 
640
  "PK": 163450700,
641
  "SG": 9314
642
  },
643
+ "language_family": "Indo-European",
644
+ "fleurs_tag": "pa_in"
645
  },
646
  {
647
  "language_name": "Russian",
 
689
  "US": 798334,
690
  "UZ": 4279156
691
  },
692
+ "language_family": "Indo-European",
693
+ "fleurs_tag": "ru_ru"
694
  },
695
  {
696
  "language_name": "Swahili",
 
724
  "YT": 2716,
725
  "ZA": 1016
726
  },
727
+ "language_family": "Atlantic-Congo",
728
+ "fleurs_tag": "sw_ke"
729
  },
730
  {
731
  "language_name": "Indonesian",
 
752
  "ID": 170896640,
753
  "NL": 311047
754
  },
755
+ "language_family": "Austronesian",
756
+ "fleurs_tag": "id_id"
757
  },
758
  {
759
  "language_name": "German",
 
805
  "SK": 1196932,
806
  "US": 1563403
807
  },
808
+ "language_family": "Indo-European",
809
+ "fleurs_tag": "de_de"
810
  },
811
  {
812
  "language_name": "Japanese",
 
834
  "CA": 52772,
835
  "JP": 119231650
836
  },
837
+ "language_family": "Japonic",
838
+ "fleurs_tag": "ja_jp"
839
  },
840
  {
841
  "language_name": "Telugu",
 
861
  "population": {
862
  "IN": 95478480
863
  },
864
+ "language_family": "Dravidian",
865
+ "fleurs_tag": "te_in"
866
  },
867
  {
868
  "language_name": "Marathi",
 
888
  "population": {
889
  "IN": 92826300
890
  },
891
+ "language_family": "Indo-European",
892
+ "fleurs_tag": "mr_in"
893
  },
894
  {
895
  "language_name": "Javanese",
 
916
  "ID": 90788840,
917
  "MY": 391825
918
  },
919
+ "language_family": "Austronesian",
920
+ "fleurs_tag": "jv_id"
921
  },
922
  {
923
  "language_name": "Vietnamese",
 
946
  "US": 1130973,
947
  "VN": 84900318
948
  },
949
+ "language_family": "Austroasiatic",
950
+ "fleurs_tag": "vi_vn"
951
  }
952
  ]
uv.lock CHANGED
@@ -205,6 +205,15 @@ wheels = [
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