evaluation
Browse files
README.md
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@@ -8,7 +8,21 @@ datasets:
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- mozilla-foundation/common_voice_7_0
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model-index:
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- name: wav2vec2-xls-r-gn-cv7
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results:
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- mozilla-foundation/common_voice_7_0
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model-index:
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- name: wav2vec2-xls-r-gn-cv7
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice 7
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type: mozilla-foundation/common_voice_7_0
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args: pt
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metrics:
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- name: Validation WER
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type: wer
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value: 73.02
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- name: Validation CER
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type: cer
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value: 17.79
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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eval.py
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@@ -0,0 +1,137 @@
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#!/usr/bin/env python3
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import argparse
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import re
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from typing import Dict
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import torch
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from datasets import Audio, Dataset, load_dataset, load_metric
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from transformers import AutoFeatureExtractor, pipeline
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def log_results(result: Dataset, args: Dict[str, str]):
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"""DO NOT CHANGE. This function computes and logs the result metrics."""
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log_outputs = args.log_outputs
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dataset_id = "_".join(args.dataset.split("/") + [args.config, args.split])
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# load metric
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wer = load_metric("wer")
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cer = load_metric("cer")
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# compute metrics
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wer_result = wer.compute(references=result["target"], predictions=result["prediction"])
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cer_result = cer.compute(references=result["target"], predictions=result["prediction"])
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# print & log results
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result_str = f"WER: {wer_result}\n" f"CER: {cer_result}"
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print(result_str)
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with open(f"{dataset_id}_eval_results.txt", "w") as f:
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f.write(result_str)
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# log all results in text file. Possibly interesting for analysis
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if log_outputs is not None:
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pred_file = f"log_{dataset_id}_predictions.txt"
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target_file = f"log_{dataset_id}_targets.txt"
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with open(pred_file, "w") as p, open(target_file, "w") as t:
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# mapping function to write output
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def write_to_file(batch, i):
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p.write(f"{i}" + "\n")
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p.write(batch["prediction"] + "\n")
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t.write(f"{i}" + "\n")
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t.write(batch["target"] + "\n")
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result.map(write_to_file, with_indices=True)
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def normalize_text(text: str) -> str:
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"""DO ADAPT FOR YOUR USE CASE. this function normalizes the target text."""
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chars_to_ignore_regex = '[,?.!\-\;\:"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
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text = re.sub(chars_to_ignore_regex, "", text.lower())
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# In addition, we can normalize the target text, e.g. removing new lines characters etc...
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# note that order is important here!
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token_sequences_to_ignore = ["\n\n", "\n", " ", " "]
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for t in token_sequences_to_ignore:
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text = " ".join(text.split(t))
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return text
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def main(args):
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# load dataset
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dataset = load_dataset(args.dataset, args.config, split=args.split, use_auth_token=True)
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# for testing: only process the first two examples as a test
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# dataset = dataset.select(range(10))
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# load processor
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feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_id)
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sampling_rate = feature_extractor.sampling_rate
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# resample audio
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dataset = dataset.cast_column("audio", Audio(sampling_rate=sampling_rate))
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# load eval pipeline
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if args.device is None:
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args.device = 0 if torch.cuda.is_available() else -1
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asr = pipeline("automatic-speech-recognition", model=args.model_id, device=args.device)
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# map function to decode audio
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def map_to_pred(batch):
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prediction = asr(
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batch["audio"]["array"], chunk_length_s=args.chunk_length_s, stride_length_s=args.stride_length_s
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)
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batch["prediction"] = prediction["text"]
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batch["target"] = normalize_text(batch["sentence"])
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return batch
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# run inference on all examples
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result = dataset.map(map_to_pred, remove_columns=dataset.column_names)
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# compute and log_results
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# do not change function below
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log_results(result, args)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
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)
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parser.add_argument(
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"--dataset",
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type=str,
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required=True,
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help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
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)
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parser.add_argument(
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"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
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)
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parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
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parser.add_argument(
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"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
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)
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parser.add_argument(
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"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
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)
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parser.add_argument(
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"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
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)
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parser.add_argument(
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"--device",
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type=int,
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default=None,
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help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
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)
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args = parser.parse_args()
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main(args)
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eval.sh
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./eval.py --model_id lgris/wav2vec2-xls-r-gn-cv7 --dataset mozilla-foundation/common_voice_7_0 --config gn --split validation --log_outputs
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log_mozilla-foundation_common_voice_7_0_gn_validation_predictions.txt
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0
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nykotererei ho oysã porã
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1
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mávapape mba'e ikuakapáva poyvórape
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+
2
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ikatuhá peve ahejande chupe
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+
3
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8 |
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povákuri iperiumguekuéra ndive tava parauguavýpe
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+
4
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kui mba'e oiku vapynandiovasry ha oñemombeporaguáiva
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+
5
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ohopáma piko
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+
6
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nomberekyra
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+
7
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he rembiapokue kuy
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8
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ñe'epyrũrãme ndahe raivaʼekue
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+
9
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ha upépe oĩjepe oheiakaʼe me lipiráriepe
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+
10
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romaña porãva ojuhe noroñe'ẽi ojupe
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+
11
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na chemandu'ái
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+
12
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hoyhepyme'ẽ chupe
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13
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oipurúrheikuái ka'a ha ho'ute rere
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+
14
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oré rotuviatã lunekue
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15
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mbapéipate hendume umi rugárpe
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+
16
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mávapape mba'e ñanembopyʼáruribéga
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+
17
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+
omosã umi mbaʼekuaa
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+
18
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+
po'ã ka hape aréko heta mba'etuja ndaipotãvéi uma
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+
19
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péia ha'e
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+
20
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+
mba'e riko chépe
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+
21
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+
aikéma katu porapýpe
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+
22
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+
nápepe oĩ cherajyla pinsésa
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+
23
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ohasahárupi a'ãraña voo karai purvensio
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+
24
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aoñetépaʼere
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+
25
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tata guasu opu'ãvo 'ojahéi tekoháre
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+
26
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upéima niko ñelmo'ãku oñeñarundujevýva
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+
27
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ohecha mba'éichapa ha'e oñemboja ha oike guasura'y mbive yro'õme
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+
28
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ño nemborieahúko na ñandiapo'ãivavoi
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+
29
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ha oporeíma hesy
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+
30
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+
oopa umi mba'ére niko tapichakuéra ohaihúi chupe
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+
31
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+
ndaipóri ivy pome'ẽ'aréva hi'a
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+
32
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+
aravo opa jeike hagua
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+
33
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+
oimérez nriko hovah hína plá ta yguy mba'e
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+
34
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a upéicha avei ndoipotãete uñe'ẽkui mba'ekuéra ndive
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+
35
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+
huungy ha morote
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log_mozilla-foundation_common_voice_7_0_gn_validation_targets.txt
ADDED
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+
0
|
2 |
+
mmm ko terere hoysã porã
|
3 |
+
1
|
4 |
+
mávapa pe mbaʼe ipuʼakapáva ko yvórape
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5 |
+
2
|
6 |
+
ikatuha peve ahejánte chupe
|
7 |
+
3
|
8 |
+
ovákuri ipehẽnguekuéra ndive táva paraguaýpe
|
9 |
+
4
|
10 |
+
kuimba'e oikóva pynandi hovasy ha oñemonde paraguáiva
|
11 |
+
5
|
12 |
+
ohopámapiko
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13 |
+
6
|
14 |
+
ndekyra
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15 |
+
7
|
16 |
+
hembiapokue ypy
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17 |
+
8
|
18 |
+
ñepyrũrãme ndaheraivaʼekue
|
19 |
+
9
|
20 |
+
ha upépe oĩjepe avei academia literariape
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21 |
+
10
|
22 |
+
romaña porã ojuehe noroñe'ẽi ojupe
|
23 |
+
11
|
24 |
+
nachemandu'ái
|
25 |
+
12
|
26 |
+
ohepyme'ẽ chupe
|
27 |
+
13
|
28 |
+
oipuru hikuái ka'a ha ho'u terere
|
29 |
+
14
|
30 |
+
ore rostudiáta luneskue
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31 |
+
15
|
32 |
+
mbaʼépa pehendúne umi lugarpe
|
33 |
+
16
|
34 |
+
mávapa pe mbaʼe ñanembopyʼaroryvéva
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35 |
+
17
|
36 |
+
omosã umi mbaʼekuaa
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37 |
+
18
|
38 |
+
ko'ã cajape areko heta mba'e tuja ndaipotavéima
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39 |
+
19
|
40 |
+
péa ha'e
|
41 |
+
20
|
42 |
+
mba'épiko chéve
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43 |
+
21
|
44 |
+
aikéma katu korapýpe
|
45 |
+
22
|
46 |
+
napépe oĩ che rajy la princesa
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47 |
+
23
|
48 |
+
ohasaha rupi ára ñavõ karai prudencio
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49 |
+
24
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50 |
+
añetépa ere
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51 |
+
25
|
52 |
+
tata guasu opu'ãvo ojahéi tekoháre
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53 |
+
26
|
54 |
+
upéima niko ñaimo'ã ku añeñandujeýva
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55 |
+
27
|
56 |
+
ohecha mba'éichapa ha'e oñemboja ha oike guasu ra'y ndive yno'õme
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57 |
+
28
|
58 |
+
ñande mboriahúko nañandepo'aivavoi
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59 |
+
29
|
60 |
+
ha ohoreíma heseve
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61 |
+
30
|
62 |
+
opa umi mba'ére niko tapichakuéra ohayhu ichupe
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63 |
+
31
|
64 |
+
ndaipóri yvy ome'ẽ'aréva hi'a
|
65 |
+
32
|
66 |
+
aravo ojeike haguã
|
67 |
+
33
|
68 |
+
oiméne niko kóva hína pláta yvyguy mba'e
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69 |
+
34
|
70 |
+
ha upéicha avei ndoipotaiete oñe'ẽ kuimba'ekuéra ndive
|
71 |
+
35
|
72 |
+
hũngy ha morotĩ
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mozilla-foundation_common_voice_7_0_gn_validation_eval_results.txt
ADDED
@@ -0,0 +1,2 @@
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1 |
+
WER: 0.7302631578947368
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2 |
+
CER: 0.17799043062200956
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